Artificial Intelligence Ontology
type: Ontology
version IRI: aio.owl
description: This ontology models classes and relationships describing deep learning networks, their component layers and activation functions, as well as potential biases.
license: http://creativecommons.org/licenses/by/4.0/
version info: 2024-11-11

definition
type: AnnotationProperty
definition: The official definition, explaining the meaning of a class or property. Shall be Aristotelian, formalized and normalized. Can be augmented with colloquial definitions.

description
type: AnnotationProperty
definition: Description may include but is not limited to: an abstract, table of contents, reference to a graphical representation of content or a free-text account of the content.

license
type: AnnotationProperty
definition: A legal document giving official permission to do something with the resource.

title
type: AnnotationProperty
definition: A name given to the resource.

subset_property
type: AnnotationProperty

ActivationFunctionSubset
type: AnnotationProperty
comment: Activation Function Subset
sub property of: subset_property

BiasSubset
type: AnnotationProperty
comment: Bias Subset
sub property of: subset_property

ClassSubset
type: AnnotationProperty
comment: Class Subset
sub property of: subset_property

FunctionSubset
type: AnnotationProperty
comment: Function Subset
sub property of: subset_property

InstanceNormalizationLayerSubset
type: AnnotationProperty
comment: Instance Normalization Layer Subset
sub property of: subset_property

LayerSubset
type: AnnotationProperty
comment: Layer Subset
sub property of: subset_property

MachineLearningSubset
type: AnnotationProperty
comment: Machine Learning Subset
sub property of: subset_property

ModelSubset
type: AnnotationProperty
comment: Model Subset
sub property of: subset_property

NetworkSubset
type: AnnotationProperty
comment: Network Subset
sub property of: subset_property

PreprocessingSubset
type: AnnotationProperty
comment: Preprocessing Subset
sub property of: subset_property

part of
type: ObjectProperty
definition: A core relation that holds between a part and its whole

has part
type: ObjectProperty
type: DatatypeProperty
definition: A core relation that holds between a whole and its part

AbstractRNNCell
type: Class
subclass of: Layer
definition: A layer representing an RNN cell that is the base class for implementing RNN cells with custom behavior.
in subset: LayerSubset

Axiom
annotated source: AbstractRNNCell
annotated property: definition
annotated target: A layer representing an RNN cell that is the base class for implementing RNN cells with custom behavior.
has db xref: AbstractRNNCell

Activation Layer
type: Class
subclass of: Layer
definition: A layer that applies an activation function to an output.
in subset: LayerSubset
comment: Applies an activation function to an output.

Axiom
annotated source: Activation Layer
annotated property: definition
annotated target: A layer that applies an activation function to an output.
has db xref: Activation

Active Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that interactively query a user or another information source to label new data points with the desired outputs.
has exact synonym: Query Learning
in subset: MachineLearningSubset

Axiom
annotated source: Active Learning
annotated property: definition
annotated target: A machine learning task focused on methods that interactively query a user or another information source to label new data points with the desired outputs.
has db xref: Active learning (machine learning)

Activity Bias
type: Class
subclass of: Use And Interpretation Bias
definition: A use and interpretation bias occurring when systems/platforms get training data from their most active users rather than less active or inactive users.
in subset: BiasSubset

Axiom
annotated source: Activity Bias
annotated property: definition
annotated target: A use and interpretation bias occurring when systems/platforms get training data from their most active users rather than less active or inactive users.
has db xref: Interpretive bias

ActivityRegularization Layer
type: Class
subclass of: Regularization Layer
definition: A regularization layer that applies an update to the cost function based on input activity.
in subset: LayerSubset

Axiom
annotated source: ActivityRegularization Layer
annotated property: definition
annotated target: A regularization layer that applies an update to the cost function based on input activity.
has db xref: ActivityRegularization

AdaptiveAvgPool1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 1D adaptive average pooling over an input signal composed of several input planes.
has exact synonym: AdaptiveAvgPool1D
in subset: LayerSubset

Axiom
annotated source: AdaptiveAvgPool1D Layer
annotated property: definition
annotated target: A pooling layer that applies a 1D adaptive average pooling over an input signal composed of several input planes.
has db xref: pooling-layers

AdaptiveAvgPool2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 2D adaptive average pooling over an input signal composed of several input planes.
has exact synonym: AdaptiveAvgPool2D
in subset: LayerSubset

Axiom
annotated source: AdaptiveAvgPool2D Layer
annotated property: definition
annotated target: A pooling layer that applies a 2D adaptive average pooling over an input signal composed of several input planes.
has db xref: pooling-layers

AdaptiveAvgPool3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 3D adaptive average pooling over an input signal composed of several input planes.
has exact synonym: AdaptiveAvgPool3D
in subset: LayerSubset

Axiom
annotated source: AdaptiveAvgPool3D Layer
annotated property: definition
annotated target: A pooling layer that applies a 3D adaptive average pooling over an input signal composed of several input planes.
has db xref: pooling-layers

AdaptiveMaxPool1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 1D adaptive max pooling over an input signal composed of several input planes.
has exact synonym: AdaptiveMaxPool1D
in subset: LayerSubset

Axiom
annotated source: AdaptiveMaxPool1D Layer
annotated property: definition
annotated target: A pooling layer that applies a 1D adaptive max pooling over an input signal composed of several input planes.
has db xref: pooling-layers

AdaptiveMaxPool2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 2D adaptive max pooling over an input signal composed of several input planes.
has exact synonym: AdaptiveMaxPool2D
in subset: LayerSubset

Axiom
annotated source: AdaptiveMaxPool2D Layer
annotated property: definition
annotated target: A pooling layer that applies a 2D adaptive max pooling over an input signal composed of several input planes.
has db xref: pooling-layers

AdaptiveMaxPool3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 3D adaptive max pooling over an input signal composed of several input planes.
has exact synonym: AdaptiveMaxPool3D
in subset: LayerSubset

Axiom
annotated source: AdaptiveMaxPool3D Layer
annotated property: definition
annotated target: A pooling layer that applies a 3D adaptive max pooling over an input signal composed of several input planes.
has db xref: pooling-layers

Add Layer
type: Class
subclass of: Merging Layer
definition: A merging layer that adds a list of inputs taking as input a list of tensors all of the same shape.
in subset: LayerSubset
comment: Layer that adds a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).

Axiom
annotated source: Add Layer
annotated property: definition
annotated target: A merging layer that adds a list of inputs taking as input a list of tensors all of the same shape.
has db xref: Add

Addition Layer
type: Class
subclass of: Layer
definition: A layer that adds inputs from one or more other layers to cells or neurons of a target layer.
in subset: LayerSubset

AdditiveAttention Layer
type: Class
subclass of: Attention Layer
definition: An attention layer that implements additive attention also known as Bahdanau-style attention.
in subset: LayerSubset
comment: Additive attention layer, a.k.a. Bahdanau-style attention.

Axiom
annotated source: AdditiveAttention Layer
annotated property: definition
annotated target: An attention layer that implements additive attention also known as Bahdanau-style attention.
has db xref: AdditiveAttention

AlphaDropout Layer
type: Class
subclass of: Regularization Layer
definition: A regularization layer that applies Alpha Dropout to the input keeping mean and variance of inputs to ensure self-normalizing property.
in subset: LayerSubset
comment: Applies Alpha Dropout to the input. Alpha Dropout is a Dropout that keeps mean and variance of inputs to their original values, in order to ensure the self-normalizing property even after this dropout. Alpha Dropout fits well to Scaled Exponential Linear Units by randomly setting activations to the negative saturation value.

Axiom
annotated source: AlphaDropout Layer
annotated property: definition
annotated target: A regularization layer that applies Alpha Dropout to the input keeping mean and variance of inputs to ensure self-normalizing property.
has db xref: AlphaDropout

Amplification Bias
type: Class
subclass of: Processing Bias
definition: A processing bias arising when the distribution over prediction outputs is skewed compared to the prior distribution of the prediction target.
in subset: BiasSubset

Axiom
annotated source: Amplification Bias
annotated property: definition
annotated target: A processing bias arising when the distribution over prediction outputs is skewed compared to the prior distribution of the prediction target.
has db xref: d1e5237

Anchoring Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by the influence of a reference point or anchor on decisions leading to insufficient adjustment from that anchor point.
in subset: BiasSubset

Axiom
annotated source: Anchoring Bias
annotated property: definition
annotated target: An individual bias characterized by the influence of a reference point or anchor on decisions leading to insufficient adjustment from that anchor point.
has db xref: NIST.SP.1270

Annotator Reporting Bias
type: Class
subclass of: Individual Bias
definition: An individual bias occurring when users rely on automation as a heuristic replacement for their own information seeking and processing.
in subset: BiasSubset

Axiom
annotated source: Annotator Reporting Bias
annotated property: definition
annotated target: An individual bias occurring when users rely on automation as a heuristic replacement for their own information seeking and processing.
has db xref: NIST.SP.1270

Artificial Neural Network
type: Class
subclass of: Network
definition: A network based on a collection of connected units called artificial neurons modeled after biological neurons.
has exact synonym: ANN
has exact synonym: NN
in subset: NetworkSubset
comment: An artificial neural network (ANN) is based on a collection of connected units or nodes called artificial neurons, modeled after biological neurons, with connections transmitting signals processed by non-linear functions.

Axiom
annotated source: Artificial Neural Network
annotated property: definition
annotated target: A network based on a collection of connected units called artificial neurons modeled after biological neurons.
has db xref: Artificial neural network

Association Rule Learning
type: Class
subclass of: Supervised Learning
definition: A supervised learning focused on a rule-based approach for discovering interesting relations between variables in large databases.
in subset: MachineLearningSubset

Axiom
annotated source: Association Rule Learning
annotated property: definition
annotated target: A supervised learning focused on a rule-based approach for discovering interesting relations between variables in large databases.
has db xref: Association rule learning

Attention Layer
type: Class
subclass of: Layer
definition: A layer that implements dot-product attention also known as Luong-style attention.
in subset: LayerSubset
comment: Dot-product attention layer, a.k.a. Luong-style attention.

Axiom
annotated source: Attention Layer
annotated property: definition
annotated target: A layer that implements dot-product attention also known as Luong-style attention.
has db xref: Attention

Auto Encoder Network
type: Class
subclass of: Unsupervised Pretrained Network
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Matched Input-Output Layer
definition: An unsupervised pretrained network that learns efficient codings of unlabeled data by training to ignore insignificant data and regenerate input from encoding.
has exact synonym: AE
in subset: NetworkSubset
comment: Layers: Input, Hidden, Matched Output-Input

Axiom
annotated source: Auto Encoder Network
annotated property: definition
annotated target: An unsupervised pretrained network that learns efficient codings of unlabeled data by training to ignore insignificant data and regenerate input from encoding.
has db xref: Autoencoder

Automation Complacency Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by over-reliance on automated systems leading to attenuated human skills.
has exact synonym: Automation Complaceny
in subset: BiasSubset
comment: Over-reliance on automated systems, leading to attenuated human skills, such as with spelling and autocorrect.

Axiom
annotated source: Automation Complacency Bias
annotated property: definition
annotated target: An individual bias characterized by over-reliance on automated systems leading to attenuated human skills.
has db xref: NIST.SP.1270

Autoregressive Conditional Heteroskedasticity
type: Class
subclass of: Model
definition: A model that describes the variance of the current error term as a function of the previous periods' error terms, capturing volatility clustering. Used for time series data.
has exact synonym: ARCH
in subset: ModelSubset

Autoregressive Distributed Lag
type: Class
subclass of: Model
definition: A model that includes lagged values of both the dependent variable and one or more independent variables, capturing dynamic relationships over time. Used in time series analysis.
has exact synonym: ARDL
in subset: ModelSubset

Autoregressive Integrated Moving Average
type: Class
subclass of: Model
definition: A model which combines autoregression (AR), differencing (I), and moving average (MA) components. Used for analyzing and forecasting time series data.
has exact synonym: ARIMA
in subset: ModelSubset

Autoregressive Language Model
type: Class
subclass of: Language Model
definition: A language model that generates text sequentially predicting one token at a time based on the previously generated tokens excelling at natural language generation tasks by modeling the probability distribution over sequences of tokens.
has related synonym: generative language model
has related synonym: sequence-to-sequence model
in subset: ModelSubset

Autoregressive Moving Average
type: Class
subclass of: Model
definition: A model that combines autoregressive (AR) and moving average (MA) components to represent time series data, suitable for stationary series without the need for differencing.
has exact synonym: ARMA
in subset: ModelSubset

Availability Heuristic Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by a mental shortcut where easily recalled information is overweighted in judgment and decision-making.
has exact synonym: Availability Bias
has exact synonym: Availability Heuristic
in subset: BiasSubset

Axiom
annotated source: Availability Heuristic Bias
annotated property: definition
annotated target: An individual bias characterized by a mental shortcut where easily recalled information is overweighted in judgment and decision-making.
has db xref: NIST.SP.1270

Average Layer
type: Class
subclass of: Merging Layer
definition: A merging layer that averages a list of inputs element-wise taking as input a list of tensors all of the same shape.
in subset: LayerSubset
comment: Layer that averages a list of inputs element-wise. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).

Axiom
annotated source: Average Layer
annotated property: definition
annotated target: A merging layer that averages a list of inputs element-wise taking as input a list of tensors all of the same shape.
has db xref: Average

AveragePooling1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs average pooling for temporal data.
has exact synonym: AvgPool1D
in subset: LayerSubset
comment: Average pooling for temporal data. Downsamples the input representation by taking the average value over the window defined by pool_size. The window is shifted by strides. The resulting output when using "valid" padding option has a shape of: output_shape = (input_shape - pool_size + 1) / strides). The resulting output shape when using the "same" padding option is: output_shape = input_shape / strides.

Axiom
annotated source: AveragePooling1D Layer
annotated property: definition
annotated target: A pooling layer that performs average pooling for temporal data.
has db xref: AveragePooling1D

AveragePooling2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs average pooling for spatial data.
has exact synonym: AvgPool2D
in subset: LayerSubset
comment: Average pooling operation for spatial data. Downsamples the input along its spatial dimensions (height and width) by taking the average value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension. The resulting output when using "valid" padding option has a shape (number of rows or columns) of: output_shape = math.floor((input_shape - pool_size) / strides) + 1 (when input_shape >= pool_size). The resulting output shape when using the "same" padding option is: output_shape = math.floor((input_shape - 1) / strides) + 1.

Axiom
annotated source: AveragePooling2D Layer
annotated property: definition
annotated target: A pooling layer that performs average pooling for spatial data.
has db xref: AveragePooling2D

AveragePooling3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs average pooling for 3D data (spatial or spatio-temporal).
has exact synonym: AvgPool3D
in subset: LayerSubset
comment: Average pooling operation for 3D data (spatial or spatio-temporal). Downsamples the input along its spatial dimensions (depth, height, and width) by taking the average value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension.

Axiom
annotated source: AveragePooling3D Layer
annotated property: definition
annotated target: A pooling layer that performs average pooling for 3D data (spatial or spatio-temporal).
has db xref: AveragePooling3D

AvgPool1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 1D average pooling over an input signal composed of several input planes.
has exact synonym: AvgPool1D
in subset: LayerSubset

Axiom
annotated source: AvgPool1D Layer
annotated property: definition
annotated target: A pooling layer that applies a 1D average pooling over an input signal composed of several input planes.
has db xref: pooling-layers

AvgPool2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 2D average pooling over an input signal composed of several input planes.
has exact synonym: AvgPool2D
in subset: LayerSubset

Axiom
annotated source: AvgPool2D Layer
annotated property: definition
annotated target: A pooling layer that applies a 2D average pooling over an input signal composed of several input planes.
has db xref: pooling-layers

AvgPool3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 3D average pooling over an input signal composed of several input planes.
has exact synonym: AvgPool3D
in subset: LayerSubset

Axiom
annotated source: AvgPool3D Layer
annotated property: definition
annotated target: A pooling layer that applies a 3D average pooling over an input signal composed of several input planes.
has db xref: pooling-layers

Backfed Input Layer
type: Class
subclass of: Input Layer
definition: An input layer that receives values from another layer.
in subset: LayerSubset

BatchNorm1D Layer
type: Class
subclass of: BatchNormalization Layer
definition: A batch normalization layer that applies Batch Normalization over a 2D or 3D input.
has exact synonym: BatchNorm1D
in subset: LayerSubset
comment: Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

Axiom
annotated source: BatchNorm1D Layer
annotated property: definition
annotated target: A batch normalization layer that applies Batch Normalization over a 2D or 3D input.
has db xref: normalization-layers

BatchNorm2D Layer
type: Class
subclass of: BatchNormalization Layer
definition: A batch normalization layer that applies Batch Normalization over a 4D input.
has exact synonym: BatchNorm2D
in subset: LayerSubset
comment: Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

Axiom
annotated source: BatchNorm2D Layer
annotated property: definition
annotated target: A batch normalization layer that applies Batch Normalization over a 4D input.
has db xref: normalization-layers

BatchNorm3D Layer
type: Class
subclass of: BatchNormalization Layer
definition: A batch normalization layer that applies Batch Normalization over a 5D input.
has exact synonym: BatchNorm3D
in subset: LayerSubset
comment: Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

Axiom
annotated source: BatchNorm3D Layer
annotated property: definition
annotated target: A batch normalization layer that applies Batch Normalization over a 5D input.
has db xref: normalization-layers

BatchNormalization Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that normalizes its inputs applying a transformation that maintains the mean close to 0 and the standard deviation close to 1.
has exact synonym: BatchNorm
in subset: LayerSubset
comment: Layer that normalizes its inputs. Batch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1. Importantly, batch normalization works differently during training and during inference. During training (i.e. when using fit() or when calling the layer/model with the argument training=True), the layer normalizes its output using the mean and standard deviation of the current batch of inputs. That is to say, for each channel being normalized, the layer returns gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta, where: epsilon is small constant (configurable as part of the constructor arguments), gamma is a learned scaling factor (initialized as 1), which can be disabled by passing scale=False to the constructor. beta is a learned offset factor (initialized as 0), which can be disabled by passing center=False to the constructor. During inference (i.e. when using evaluate() or predict() or when calling the layer/model with the argument training=False (which is the default), the layer normalizes its output using a moving average of the mean and standard deviation of the batches it has seen during training. That is to say, it returns gamma * (batch - self.moving_mean) / sqrt(self.moving_var + epsilon) + beta. self.moving_mean and self.moving_var are non-trainable variables that are updated each time the layer in called in training mode, as such: moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum) moving_var = moving_var * momentum + var(batch) * (1 - momentum).

Axiom
annotated source: BatchNormalization Layer
annotated property: definition
annotated target: A normalization layer that normalizes its inputs applying a transformation that maintains the mean close to 0 and the standard deviation close to 1.
has db xref: BatchNormalization

Bayesian Network
type: Class
subclass of: Network
definition: A network that is a probabilistic graphical model representing variables and their conditional dependencies via a directed acyclic graph.
in subset: NetworkSubset

Axiom
annotated source: Bayesian Network
annotated property: definition
annotated target: A network that is a probabilistic graphical model representing variables and their conditional dependencies via a directed acyclic graph.
has db xref: Bayesian network

Behavioral Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by systematic distortions in user behavior across platforms or contexts or across users represented in different datasets.
in subset: BiasSubset
comment: Systematic distortions in user behavior across platforms or contexts, or across users represented in different datasets.

Axiom
annotated source: Behavioral Bias
annotated property: definition
annotated target: An individual bias characterized by systematic distortions in user behavior across platforms or contexts or across users represented in different datasets.
has db xref: NIST.SP.1270

Bias
type: Class
subclass of: Thing
definition: A systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others.
in subset: BiasSubset

Axiom
annotated source: Bias
annotated property: definition
annotated target: A systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others.
has db xref: bias

Biclustering
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that simultaneously cluster the rows and columns of a matrix to identify submatrices with coherent patterns.
has exact synonym: Block Clustering
has exact synonym: Co-clustering
has exact synonym: Joint Clustering
has exact synonym: Two-mode Clustering
has exact synonym: Two-way Clustering
in subset: MachineLearningSubset

Axiom
annotated source: Biclustering
annotated property: definition
annotated target: A machine learning task focused on methods that simultaneously cluster the rows and columns of a matrix to identify submatrices with coherent patterns.
has db xref: Biclustering

Bidirectional Layer
type: Class
subclass of: Recurrent Layer
definition: A recurrent layer that is a bidirectional wrapper for RNNs.
in subset: LayerSubset
comment: Bidirectional wrapper for RNNs.

Axiom
annotated source: Bidirectional Layer
annotated property: definition
annotated target: A recurrent layer that is a bidirectional wrapper for RNNs.
has db xref: Bidirectional

Bidirectional Transformer Language Model
type: Class
subclass of: Transformer Language Model
definition: A transformer language model such as BERT that uses the transformer architecture to build deep bidirectional representations by predicting masked tokens based on their context.
has exact synonym: BERT
has exact synonym: Bidirectional Transformer LM
in subset: ModelSubset

Axiom
annotated source: Bidirectional Transformer Language Model
annotated property: definition
annotated target: A transformer language model such as BERT that uses the transformer architecture to build deep bidirectional representations by predicting masked tokens based on their context.
has db xref: https://arxiv.org/abs/1810.04805
has db xref: BERT (language model)

Binary Classification
type: Class
subclass of: Classification
definition: A classification focused on methods that classify elements into two groups based on a classification rule.
in subset: MachineLearningSubset

Axiom
annotated source: Binary Classification
annotated property: definition
annotated target: A classification focused on methods that classify elements into two groups based on a classification rule.
has db xref: Binary classification

Boltzmann Machine Network
type: Class
subclass of: Symmetrically Connected Network
subclass of: Restriction
  on property: has part
  some values from: Backfed Input Layer
subclass of: Restriction
  on property: has part
  some values from: Probabilistic Hidden Layer
definition: A symmetrically connected network that is a type of stochastic recurrent neural network and Markov random field.
has exact synonym: BM
has exact synonym: Sherrington–Kirkpatrick model with external field
has exact synonym: stochastic Hopfield network with hidden units
has exact synonym: stochastic Ising-Lenz-Little model
in subset: NetworkSubset
comment: Layers: Backfed Input, Probabilistic Hidden

Axiom
annotated source: Boltzmann Machine Network
annotated property: definition
annotated target: A symmetrically connected network that is a type of stochastic recurrent neural network and Markov random field.
has db xref: Boltzmann machine

Categorical Features Preprocessing Layer
type: Class
subclass of: Layer
definition: A layer that performs categorical data preprocessing operations.
in subset: LayerSubset

Axiom
annotated source: Categorical Features Preprocessing Layer
annotated property: definition
annotated target: A layer that performs categorical data preprocessing operations.
has db xref: preprocessing layers

CategoryEncoding Layer
type: Class
subclass of: Categorical Features Preprocessing Layer
definition: A categorical features preprocessing layer that encodes integer features providing options for condensing data into a categorical encoding.
in subset: LayerSubset
comment: A preprocessing layer which encodes integer features. This layer provides options for condensing data into a categorical encoding when the total number of tokens are known in advance. It accepts integer values as inputs, and it outputs a dense or sparse representation of those inputs. For integer inputs where the total number of tokens is not known, use tf.keras.layers.IntegerLookup instead.

Axiom
annotated source: CategoryEncoding Layer
annotated property: definition
annotated target: A categorical features preprocessing layer that encodes integer features providing options for condensing data into a categorical encoding.
has db xref: CategoryEncoding

Causal Graphical Model
type: Class
subclass of: Probabilistic Graphical Model
definition: A probabilistic graphical model used to encode assumptions about the data-generating process.
has exact synonym: Casaul Bayesian Network
has exact synonym: Casaul Graph
has exact synonym: DAG
has exact synonym: Directed Acyclic Graph
has exact synonym: Path Diagram
in subset: MachineLearningSubset

Axiom
annotated source: Causal Graphical Model
annotated property: definition
annotated target: A probabilistic graphical model used to encode assumptions about the data-generating process.
has db xref: Causal graph

Causal LLM
type: Class
subclass of: Large Language Model
definition: A large language model that only attends to previous tokens in the sequence when generating text modeling the probability distribution autoregressively from left-to-right or causally.
has exact synonym: Causal Large Language Model
has related synonym: autoregressive
has related synonym: unidirectional
in subset: ModelSubset

CenterCrop Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that crops the central portion of images to a target size.
in subset: LayerSubset
comment: A preprocessing layer which crops images. This layers crops the central portion of the images to a target size. If an image is smaller than the target size, it will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.

Axiom
annotated source: CenterCrop Layer
annotated property: definition
annotated target: An image preprocessing layer that crops the central portion of images to a target size.
has db xref: CenterCrop

Classification
type: Class
subclass of: Supervised Learning
definition: A supervised learning focused on methods that distinguish and distribute kinds of "things" into different groups.
in subset: MachineLearningSubset
comment: Methods that distinguish and distribute kinds of "things" into different groups.

Axiom
annotated source: Classification
annotated property: definition
annotated target: A supervised learning focused on methods that distinguish and distribute kinds of "things" into different groups.
has db xref: Classification (general theory)

Cleaning
type: Class
subclass of: Data Preparation
definition: A data preparation that removes noise inconsistencies and irrelevant information from data to enhance its quality and prepare it for analysis or further processing.
has exact synonym: Data Cleansing
has exact synonym: Standardization
has related synonym: Data cleaning
has related synonym: Text normalization
in subset: PreprocessingSubset

Clustering
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that group a set of objects such that objects in the same group are more similar to each other than to those in other groups.
has exact synonym: Cluster analysis
in subset: MachineLearningSubset

Axiom
annotated source: Clustering
annotated property: definition
annotated target: A machine learning task focused on methods that group a set of objects such that objects in the same group are more similar to each other than to those in other groups.
has db xref: Cluster analysis

Cognitive Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by deviations from rational judgment and decision-making including adaptive mental shortcuts known as heuristics.
in subset: BiasSubset

Axiom
annotated source: Cognitive Bias
annotated property: definition
annotated target: An individual bias characterized by deviations from rational judgment and decision-making including adaptive mental shortcuts known as heuristics.
has db xref: NIST.SP.1270

Compositional Generalization LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained to understand and recombine the underlying compositional structures in language enabling better generalization to novel combinations and out-of-distribution examples.
has exact synonym: Compositional Generalization Large Language Model
has related synonym: out-of-distribution generalization
has related synonym: systematic generalization
in subset: ModelSubset

Computational Bias
type: Class
subclass of: Bias
definition: A bias caused by differences between results and facts in the process of data analysis (including the source of data the estimator chose) and analysis methods.
has exact synonym: Statistical Bias
in subset: BiasSubset

Axiom
annotated source: Computational Bias
annotated property: definition
annotated target: A bias caused by differences between results and facts in the process of data analysis (including the source of data the estimator chose) and analysis methods.
has db xref: NIST.SP.1270

Concatenate Layer
type: Class
subclass of: Merging Layer
definition: A merging layer that concatenates a list of inputs taking as input a list of tensors all of the same shape except for the concatenation axis.
in subset: LayerSubset
comment: Layer that concatenates a list of inputs. It takes as input a list of tensors, all of the same shape except for the concatenation axis, and returns a single tensor that is the concatenation of all inputs.

Axiom
annotated source: Concatenate Layer
annotated property: definition
annotated target: A merging layer that concatenates a list of inputs taking as input a list of tensors all of the same shape except for the concatenation axis.
has db xref: Concatenate

Concept Drift Bias
type: Class
subclass of: Use And Interpretation Bias
definition: A use and interpretation bias due to the use of a system outside its planned domain of application causing performance gaps between laboratory settings and the real world.
has exact synonym: Concept Drift
in subset: BiasSubset

Axiom
annotated source: Concept Drift Bias
annotated property: definition
annotated target: A use and interpretation bias due to the use of a system outside its planned domain of application causing performance gaps between laboratory settings and the real world.
has db xref: NIST.SP.1270

Confirmation Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by the tendency to prefer information that confirms existing beliefs influencing the search for interpretation of and recall of information.
in subset: BiasSubset
comment: The tendency to prefer information that confirms existing beliefs, influencing the search for, interpretation of, and recall of information.

Axiom
annotated source: Confirmation Bias
annotated property: definition
annotated target: An individual bias characterized by the tendency to prefer information that confirms existing beliefs influencing the search for interpretation of and recall of information.
has db xref: NIST.SP.1270

Consumer Bias
type: Class
subclass of: Individual Bias
definition: An individual bias arising when an algorithm or platform provides users a venue to express their biases occurring from either side in a digital interaction.
in subset: BiasSubset

Axiom
annotated source: Consumer Bias
annotated property: definition
annotated target: An individual bias arising when an algorithm or platform provides users a venue to express their biases occurring from either side in a digital interaction.
has db xref: NIST.SP.1270

Content Production Bias
type: Class
subclass of: Use And Interpretation Bias
definition: A use and interpretation bias arising from structural lexical semantic and syntactic differences in user-generated content.
in subset: BiasSubset
comment: Bias from structural, lexical, semantic, and syntactic differences in user-generated content.

Axiom
annotated source: Content Production Bias
annotated property: definition
annotated target: A use and interpretation bias arising from structural lexical semantic and syntactic differences in user-generated content.
has db xref: NIST.SP.1270

Continual Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that learns sequential tasks without forgetting knowledge from preceding tasks and without access to old task data during new task training.
has exact synonym: Incremental Learning
has exact synonym: Life-Long Learning
in subset: NetworkSubset
comment: Learning a model for sequential tasks without forgetting knowledge from preceding tasks, with no access to old task data during new task training.

Axiom
annotated source: Continual Learning
annotated property: definition
annotated target: A deep neural network that learns sequential tasks without forgetting knowledge from preceding tasks and without access to old task data during new task training.
has db xref: continual-learning

Continual Learning LLM
type: Class
subclass of: Large Language Model
definition: A large language model that continually acquires new knowledge and skills over time without forgetting previously learned information allowing the model to adapt and expand its capabilities as new data becomes available.
has exact synonym: CL-Large Language Model
has exact synonym: Continual Learning Large Language Model
has related synonym: catastrophic forgetting
has related synonym: lifelong learning
in subset: ModelSubset

Contrastive Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network self-supervised learning approach that learns to distinguish between similar and dissimilar data samples.
in subset: NetworkSubset
comment: Contrastive learning is a self-supervised learning approach in which the model learns to distinguish between similar and dissimilar pairs of data samples. By maximizing the similarity between positive pairs (similar samples) and minimizing the similarity between negative pairs (dissimilar samples), the model learns to capture meaningful representations of the data. This method is particularly effective for representation learning and is widely used in tasks such as image classification, clustering, and retrieval. Contrastive learning techniques often employ loss functions such as the contrastive loss or the triplet loss to achieve these objectives.

Axiom
annotated source: Contrastive Learning
annotated property: definition
annotated target: A deep neural network self-supervised learning approach that learns to distinguish between similar and dissimilar data samples.
has db xref: https://arxiv.org/abs/2202.14037

Contrastive Learning LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained to pull semantically similar samples closer together and push dissimilar samples apart in the representation space learning high-quality features useful for downstream tasks.
has related synonym: Representation learning
in subset: ModelSubset

Controllable LLM
type: Class
subclass of: Large Language Model
definition: A large language model that allows for explicit control over certain attributes of the generated text such as style tone topic or other desired characteristics through conditioning or specialized training objectives.
has exact synonym: Controllable Large Language Model
has related synonym: conditional generation
has related synonym: guided generation
in subset: ModelSubset
comment: A controllable LLM allows for explicit control over certain attributes of the generated text, such as style, tone, topic, or other desired characteristics, through conditioning or specialized training objectives.

ConvLSTM1D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements a 1D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations.
in subset: LayerSubset
comment: 1D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.

Axiom
annotated source: ConvLSTM1D Layer
annotated property: definition
annotated target: A convolutional layer that implements a 1D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations.
has db xref: ConvLSTM1D

ConvLSTM2D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements a 2D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations.
in subset: LayerSubset
comment: 2D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.

Axiom
annotated source: ConvLSTM2D Layer
annotated property: definition
annotated target: A convolutional layer that implements a 2D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations.
has db xref: ConvLSTM2D

ConvLSTM3D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements a 3D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations.
in subset: LayerSubset
comment: 3D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.

Axiom
annotated source: ConvLSTM3D Layer
annotated property: definition
annotated target: A convolutional layer that implements a 3D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations.
has db xref: ConvLSTM3D

Convolution1D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements 1D convolution (e.g. temporal convolution).
has exact synonym: Conv1D
has exact synonym: Conv1D Layer
has exact synonym: Convolution1D
has exact synonym: nn.Conv1D
in subset: LayerSubset

Axiom
annotated source: Convolution1D Layer
annotated property: definition
annotated target: A convolutional layer that implements 1D convolution (e.g. temporal convolution).
has db xref: Conv1D

Convolution1DTranspose Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements transposed 1D convolution sometimes called deconvolution.
has exact synonym: Conv1DTranspose Layer
has exact synonym: ConvTranspose1D
has exact synonym: Convolution1DTranspose
has exact synonym: nn.ConvTranspose1D
in subset: LayerSubset
comment: Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 3) for data with 128 time steps and 3 channels.

Axiom
annotated source: Convolution1DTranspose Layer
annotated property: definition
annotated target: A convolutional layer that implements transposed 1D convolution sometimes called deconvolution.
has db xref: Conv1DTranspose

Convolution2D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements 2D convolution (e.g. spatial convolution over images).
has exact synonym: Conv2D
has exact synonym: Conv2D Layer
has exact synonym: Convolution2D
has exact synonym: nn.Conv2D
in subset: LayerSubset
comment: 2D convolution layer (e.g. spatial convolution over images). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last". You can use None when a dimension has variable size.

Axiom
annotated source: Convolution2D Layer
annotated property: definition
annotated target: A convolutional layer that implements 2D convolution (e.g. spatial convolution over images).
has db xref: Conv2D

Convolution2DTranspose Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements transposed 2D convolution
has exact synonym: Conv2DTranspose Layer
has exact synonym: ConvTranspose2D
has exact synonym: Convolution2DTranspose
has exact synonym: nn.ConvTranspose2D
in subset: LayerSubset
comment: Transposed convolution layer (sometimes called Deconvolution).

Axiom
annotated source: Convolution2DTranspose Layer
annotated property: definition
annotated target: A convolutional layer that implements transposed 2D convolution
has db xref: Conv2DTranspose

Convolution3D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements 3D convolution (e.g. spatial convolution over volumes).
has exact synonym: Conv3D
has exact synonym: Conv3D Layer
has exact synonym: Convolution3D
has exact synonym: nn.Conv3D
in subset: LayerSubset
comment: 3D convolution layer (e.g. spatial convolution over volumes). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 128, 1) for 128x128x128 volumes with a single channel, in data_format="channels_last".

Axiom
annotated source: Convolution3D Layer
annotated property: definition
annotated target: A convolutional layer that implements 3D convolution (e.g. spatial convolution over volumes).
has db xref: Conv3D

Convolution3DTranspose Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that implements transposed 3D convolution
has exact synonym: Conv3DTranspose Layer
has exact synonym: ConvTranspose3D
has exact synonym: Convolution3DTranspose
has exact synonym: nn.ConvTranspose3D
in subset: LayerSubset
comment: Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 128, 3) for a 128x128x128 volume with 3 channels if data_format="channels_last".

Axiom
annotated source: Convolution3DTranspose Layer
annotated property: definition
annotated target: A convolutional layer that implements transposed 3D convolution
has db xref: Conv3DTranspose

Convolutional Layer
type: Class
subclass of: Layer
definition: A layer that contains a set of filters (or kernels) parameters of which are to be learned throughout the training.
in subset: LayerSubset
comment: A convolutional layer is the main building block of a CNN. It contains a set of filters (or kernels), parameters of which are to be learned throughout the training. The size of the filters is usually smaller than the actual image. Each filter convolves with the image and creates an activation map.

Axiom
annotated source: Convolutional Layer
annotated property: definition
annotated target: A layer that contains a set of filters (or kernels) parameters of which are to be learned throughout the training.
has db xref: convolutional-layer

Cropping1D Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that crops along the time dimension (axis 1) for 1D input.
in subset: LayerSubset
comment: Cropping layer for 1D input (e.g. temporal sequence). It crops along the time dimension (axis 1).

Axiom
annotated source: Cropping1D Layer
annotated property: definition
annotated target: A reshaping layer that crops along the time dimension (axis 1) for 1D input.
has db xref: Cropping1D

Cropping2D Layer
type: Class
subclass of: Layer
definition: A layer that crops along spatial dimensions (i.e. height and width) for 2D input.
in subset: LayerSubset
comment: Cropping layer for 2D input (e.g. picture). It crops along spatial dimensions, i.e. height and width.

Axiom
annotated source: Cropping2D Layer
annotated property: definition
annotated target: A layer that crops along spatial dimensions (i.e. height and width) for 2D input.
has db xref: Cropping2D

Cropping3D Layer
type: Class
subclass of: Layer
definition: A layer that crops along spatial dimensions (depth
in subset: LayerSubset
comment: Cropping layer for 3D data (e.g. spatial or spatio-temporal).

Axiom
annotated source: Cropping3D Layer
annotated property: definition
annotated target: A layer that crops along spatial dimensions (depth
has db xref: Cropping3D

Cross-Domain LLM
type: Class
subclass of: Large Language Model
definition: A large language model that performs well across a wide range of domains without significant loss in performance, facilitated by advanced domain adaptation techniques.
has exact synonym: Domain-General LLM
has related synonym: cross-domain transfer
has related synonym: domain adaptation
in subset: ModelSubset

Curriculum Learning
type: Class
subclass of: TrainingStrategy
definition: A training strategy in machine learning where models are trained on data in a meaningful order starting with simpler examples and gradually increasing the complexity to improve learning efficiency and model performance.
has exact synonym: Sequential Learning
has exact synonym: Structured Learning
has related synonym: Complexity grading
has related synonym: Sequential learning
in subset: TrainingStrategySubset

Curriculum Learning LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained by presenting learning examples in a meaningful order from simple to complex mimicking the learning trajectory followed by humans.
has related synonym: Learning progression
in subset: ModelSubset

Data Augmentation
type: Class
subclass of: DataEnhancement
definition: A data enhancement used to increase the diversity and quantity of training data by applying various transformations such as rotation scaling flipping and cropping to existing data samples enhancing the robustness and performance of machine learning models.
has exact synonym: Data Enrichment
has exact synonym: Data Expansion
has related synonym: Paraphrasing
has related synonym: Synonym replacement
in subset: PreprocessingSubset

Data Dredging Bias
type: Class
subclass of: Use And Interpretation Bias
definition: A use and interpretation bias where testing many hypotheses in a dataset may yield apparent statistical significance even when results are nonsignificant.
has exact synonym: Data Dredging
in subset: BiasSubset

Axiom
annotated source: Data Dredging Bias
annotated property: definition
annotated target: A use and interpretation bias where testing many hypotheses in a dataset may yield apparent statistical significance even when results are nonsignificant.
has db xref: NIST.SP.1270

DataEnhancement
type: Class
subclass of: Preprocessing
definition: A preprocessing used to improve the quality diversity and volume of data available for training machine learning models, such as data augmentation synthesis and enrichment to enhance model robustness and accuracy.
has exact synonym: AIO:PreprocessingSubset
in subset: PreprocessingSubset

Data Generation Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias arising from adding synthetic or redundant data samples to a dataset.
in subset: BiasSubset
comment: Bias from adding synthetic or redundant data samples to a dataset.

Axiom
annotated source: Data Generation Bias
annotated property: definition
annotated target: A selection and sampling bias arising from adding synthetic or redundant data samples to a dataset.
has db xref: Selection bias

Data Imputation
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that replace missing data with substituted values.
in subset: MachineLearningSubset
comment: Methods that replace missing data with substituted values.

Axiom
annotated source: Data Imputation
annotated property: definition
annotated target: A machine learning task focused on methods that replace missing data with substituted values.
has db xref: Imputation (statistics)

Data Preparation
type: Class
subclass of: Preprocessing
definition: A preprocessing that cleans, transforms and organizes raw data into a suitable format for analysis and modeling, ensuring the quality and relevance of the data for machine learning tasks.
has exact synonym: Data Assembly
has exact synonym: Data Curation
has exact synonym: Data Processing
in subset: PreprocessingSubset

Data-to-Text LLM
type: Class
subclass of: Large Language Model
definition: A large language model that generates natural language descriptions from structured data sources like tables, graphs, and knowledge bases, requiring grounding in meaning representations.
has exact synonym: Meaning representation
in subset: ModelSubset

Decision Tree
type: Class
subclass of: Classification
definition: A classification that uses a tree-like model of decisions and their possible consequences including chance event outcomes resource costs and utilities.
in subset: MachineLearningSubset
comment: A decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utilities.

Axiom
annotated source: Decision Tree
annotated property: definition
annotated target: A classification that uses a tree-like model of decisions and their possible consequences including chance event outcomes resource costs and utilities.
has db xref: Decision tree

Decoder LLM
type: Class
subclass of: Large Language Model
definition: A large language model that uses a decoder-only architecture consisting of only a decoder trained to predict the next token in a sequence given the previous tokens.
in subset: ModelSubset
comment: A decoder-only architecture consisting of only a decoder, trained to predict the next token in a sequence given the previous tokens. Unlike the encoder-decoder architecture, it does not have an explicit encoder and encodes information implicitly in the hidden state of the decoder, updated at each step of the generation process.

Axiom
annotated source: Decoder LLM
annotated property: definition
annotated target: A large language model that uses a decoder-only architecture consisting of only a decoder trained to predict the next token in a sequence given the previous tokens.
has db xref: https://www.practicalai.io/understanding-transformer-model-architectures/

Deconvolutional Network
type: Class
subclass of: Deep Neural Network
subclass of: Restriction
  on property: has part
  some values from: Convolutional Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Kernel Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
subclass of: Restriction
  on property: has part
  some values from: Pooling Layer
definition: A deep neural network that uses deconvolution for unsupervised construction of hierarchical image representations.
has exact synonym: DN
in subset: NetworkSubset
comment: Layers: Input, Kernel, Convolutional/Pool, Output

Axiom
annotated source: Deconvolutional Network
annotated property: definition
annotated target: A deep neural network that uses deconvolution for unsupervised construction of hierarchical image representations.
has db xref: https://ieeexplore.ieee.org/document/5539957

Deep Active Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that combines deep learning and active learning to maximize model performance while annotating the fewest samples possible.
has exact synonym: DeepAL
in subset: NetworkSubset

Axiom
annotated source: Deep Active Learning
annotated property: definition
annotated target: A deep neural network that combines deep learning and active learning to maximize model performance while annotating the fewest samples possible.
has db xref: 2009.00236.pdf

Deep Belief Network
type: Class
subclass of: Unsupervised Pretrained Network
subclass of: Restriction
  on property: has part
  some values from: Backfed Input Layer
subclass of: Restriction
  on property: has part
  some values from: Matched Input-Output Layer
subclass of: Restriction
  on property: has part
  some values from: Probabilistic Hidden Layer
definition: An unsupervised pretrained network composed of multiple layers of latent variables that learns to probabilistically reconstruct inputs and perform classification.
has exact synonym: DBN
in subset: NetworkSubset
comment: Layers: Backfed Input, Probabilistic Hidden, Hidden, Matched Output-Input

Axiom
annotated source: Deep Belief Network
annotated property: definition
annotated target: An unsupervised pretrained network composed of multiple layers of latent variables that learns to probabilistically reconstruct inputs and perform classification.
has db xref: Deep belief network

Deep Convolutional Inverse Graphics Network
type: Class
subclass of: Auto Encoder Network
subclass of: Restriction
  on property: has part
  some values from: Convolutional Layer
subclass of: Restriction
  on property: has part
  some values from: Kernel Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
subclass of: Restriction
  on property: has part
  some values from: Pooling Layer
subclass of: Restriction
  on property: has part
  some values from: Probabilistic Hidden Layer
definition: An autoencoder network that learns interpretable disentangled image representations through convolution and de-convolution layers trained with the stochastic gradient variational Bayes algorithm.
has exact synonym: DCIGN
in subset: NetworkSubset
comment: Layers: Input, Kernel, Convolutional/Pool, Probabilistic Hidden, Convolutional/Pool, Kernel, Output

Deep Convolutional Network
type: Class
subclass of: Deep Neural Network
subclass of: Restriction
  on property: has part
  some values from: Convolutional Layer
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Kernel Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
subclass of: Restriction
  on property: has part
  some values from: Pooling Layer
definition: A deep neural network specialized for analyzing visual imagery using shared-weight architecture and translation-equivariant feature maps.
has exact synonym: CNN
has exact synonym: ConvNet
has exact synonym: Convolutional Neural Network
has exact synonym: DCN
in subset: NetworkSubset
comment: Layers: Input, Kernel, Convolutional/Pool, Hidden, Output

Axiom
annotated source: Deep Convolutional Network
annotated property: definition
annotated target: A deep neural network specialized for analyzing visual imagery using shared-weight architecture and translation-equivariant feature maps.
has db xref: Convolutional neural network

Deep Feed-Forward Network
type: Class
subclass of: Deep Neural Network
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: A deep neural network that processes information in one direction—from input nodes through hidden nodes to output nodes—without cycles or loops.
has exact synonym: DFF
has exact synonym: MLP
has exact synonym: Multilayer Perceptoron
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: Deep Feed-Forward Network
annotated property: definition
annotated target: A deep neural network that processes information in one direction—from input nodes through hidden nodes to output nodes—without cycles or loops.
has db xref: Feedforward neural network

Deep Neural Network
type: Class
subclass of: Artificial Neural Network
definition: An artificial neural network characterized by multiple hidden layers between the input and output layers.
has exact synonym: DNN
in subset: NetworkSubset
comment: A deep neural network (DNN) is a type of artificial neural network (ANN) characterized by multiple hidden layers between the input and output layers. Each layer consists of interconnected neurons that process and transmit information. DNNs can model complex patterns and representations in data through their hierarchical structure, where each layer extracts increasingly abstract features from the input. DNNs are widely used in various applications, including image and speech recognition, natural language processing, and more, due to their ability to learn and generalize from large amounts of data.

Deep Transfer Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that relaxes the hypothesis that training data must be independent and identically distributed with test data to address insufficient training data.
in subset: NetworkSubset

Axiom
annotated source: Deep Transfer Learning
annotated property: definition
annotated target: A deep neural network that relaxes the hypothesis that training data must be independent and identically distributed with test data to address insufficient training data.
has db xref: https://arxiv.org/abs/1808.01974

Denoising Auto Encoder
type: Class
subclass of: Auto Encoder Network
subclass of: Restriction
  on property: has part
  some values from: Noisy Input Layer
definition: An autoencoder network trained to reconstruct the original undistorted input from a partially corrupted input.
has exact synonym: DAE
has exact synonym: Denoising Autoencoder
in subset: NetworkSubset
comment: Layers: Noisy Input, Hidden, Matched Output-Input

Axiom
annotated source: Denoising Auto Encoder
annotated property: definition
annotated target: An autoencoder network trained to reconstruct the original undistorted input from a partially corrupted input.
has db xref: https://doi.org/10.1145/1390156.1390294

DenseFeatures Layer
type: Class
subclass of: Layer
definition: A layer that produces a dense tensor based on given feature columns.
in subset: LayerSubset
comment: A layer that produces a dense Tensor based on given feature_columns. Generally a single example in training data is described with FeatureColumns. At the first layer of the model, this column oriented data should be converted to a single Tensor. This layer can be called multiple times with different features. This is the V2 version of this layer that uses name_scopes to create variables instead of variable_scopes. But this approach currently lacks support for partitioned variables. In that case, use the V1 version instead.

Axiom
annotated source: DenseFeatures Layer
annotated property: definition
annotated target: A layer that produces a dense tensor based on given feature columns.
has db xref: DenseFeatures

Dense Layer
type: Class
subclass of: Layer
definition: A layer that is a regular densely-connected neural network layer.
in subset: LayerSubset
comment: Just your regular densely-connected NN layer.

Axiom
annotated source: Dense Layer
annotated property: definition
annotated target: A layer that is a regular densely-connected neural network layer.
has db xref: Dense

Deployment Bias
type: Class
subclass of: Group Bias
definition: A group bias arising when systems are used as decision aids for humans since the human intermediary may act on predictions in ways that are typically not modeled in the system.
in subset: BiasSubset

Axiom
annotated source: Deployment Bias
annotated property: definition
annotated target: A group bias arising when systems are used as decision aids for humans since the human intermediary may act on predictions in ways that are typically not modeled in the system.
has db xref: NIST.SP.1270

DepthwiseConv1D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that performs depthwise 1D convolution
in subset: LayerSubset
comment: Depthwise 1D convolution. Depthwise convolution is a type of convolution in which each input channel is convolved with a different kernel (called a depthwise kernel). You can understand depthwise convolution as the first step in a depthwise separable convolution. It is implemented via the following steps: Split the input into individual channels. Convolve each channel with an individual depthwise kernel with depth_multiplier output channels. Concatenate the convolved outputs along the channels axis. Unlike a regular 1D convolution, depthwise convolution does not mix information across different input channels. The depth_multiplier argument determines how many filter are applied to one input channel. As such, it controls the amount of output channels that are generated per input channel in the depthwise step.

Axiom
annotated source: DepthwiseConv1D Layer
annotated property: definition
annotated target: A convolutional layer that performs depthwise 1D convolution
has db xref: DepthwiseConv1D

DepthwiseConv2D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that performs depthwise 2D convolution
in subset: LayerSubset
comment: Depthwise 2D convolution.

Axiom
annotated source: DepthwiseConv2D Layer
annotated property: definition
annotated target: A convolutional layer that performs depthwise 2D convolution
has db xref: DepthwiseConv2D

Detection Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias characterized by systematic differences between groups in how outcomes are determined potentially over- or underestimating effect size.
in subset: BiasSubset
comment: Systematic differences between groups in how outcomes are determined, potentially over- or underestimating effect size.

Axiom
annotated source: Detection Bias
annotated property: definition
annotated target: A selection and sampling bias characterized by systematic differences between groups in how outcomes are determined potentially over- or underestimating effect size.
has db xref: NIST.SP.1270

Dialogue LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is optimized for engaging in multi-turn conversations understanding context and generating relevant coherent responses continuously over many dialogue turns.
has exact synonym: Dialogue Large Language Model
has related synonym: conversational AI
has related synonym: multi-turn dialogue
in subset: ModelSubset

Differentiable LLM
type: Class
subclass of: Large Language Model
definition: A large language model that has an architecture amenable to full end-to-end training via backpropagation without relying on teacher forcing or unlikelihood training objectives.
has exact synonym: Differentiable Large Language Model
has related synonym: end-to-end training
has related synonym: fully backpropagable
in subset: ModelSubset

Dimensionality Reduction
type: Class
subclass of: Unsupervised Learning
definition: An unsupervised learning focused on the process of transforming data from a high-dimensional space into a lower-dimensional space while retaining meaningful properties of the original data.
has exact synonym: Dimension Reduction
in subset: MachineLearningSubset

Axiom
annotated source: Dimensionality Reduction
annotated property: definition
annotated target: An unsupervised learning focused on the process of transforming data from a high-dimensional space into a lower-dimensional space while retaining meaningful properties of the original data.
has db xref: Dimensionality reduction

Discretization Layer
type: Class
subclass of: Numerical Features Preprocessing Layer
definition: A numerical features prepreprocessing layer which buckets continuous features by ranges.
in subset: LayerSubset

Axiom
annotated source: Discretization Layer
annotated property: definition
annotated target: A numerical features prepreprocessing layer which buckets continuous features by ranges.
has db xref: Discretization

Distillation
type: Class
subclass of: TrainingStrategy
definition: A preprocessing that trains a smaller model to replicate the behavior of a larger model aiming to compress the knowledge into a more compact form without significant loss of performance.
has exact synonym: Purification
has exact synonym: Refining
has related synonym: Knowledge compression
has related synonym: Teacher-student model
in subset: PreprocessingSubset

Axiom
annotated source: Distillation
annotated property: definition
annotated target: A preprocessing that trains a smaller model to replicate the behavior of a larger model aiming to compress the knowledge into a more compact form without significant loss of performance.
has db xref: arXiv.2105.13093

Domain-Adapted LLM
type: Class
subclass of: Large Language Model
definition: A large language model which is pre-trained on a broad corpus and then fine-tuned on domain-specific data to specialize its capabilities for particular domains or applications, like scientific literature or code generation.
has exact synonym: Domain-Adapted Large Language Model
has related synonym: domain robustness
has related synonym: transfer learning
in subset: ModelSubset

Dot Layer
type: Class
subclass of: Layer
definition: A layer that computes a dot product between samples in two tensors.
in subset: LayerSubset
comment: Layer that computes a dot product between samples in two tensors. E.g. if applied to a list of two tensors a and b of shape (batch_size, n), the output will be a tensor of shape (batch_size, 1) where each entry i will be the dot product between a[i] and b[i].

Axiom
annotated source: Dot Layer
annotated property: definition
annotated target: A layer that computes a dot product between samples in two tensors.
has db xref: Dot

Dropout Layer
type: Class
subclass of: Regularization Layer
definition: A regularization layer that applies Dropout to the input
in subset: LayerSubset
comment: Applies Dropout to the input. The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by 1/(1 - rate) such that the sum over all inputs is unchanged. Note that the Dropout layer only applies when training is set to True such that no values are dropped during inference. When using model.fit, training will be appropriately set to True automatically, and in other contexts, you can set the kwarg explicitly to True when calling the layer. (This is in contrast to setting trainable=False for a Dropout layer. trainable does not affect the layer's behavior, as Dropout does not have any variables/weights that can be frozen during training.)

Axiom
annotated source: Dropout Layer
annotated property: definition
annotated target: A regularization layer that applies Dropout to the input
has db xref: Dropout

Dunning-Kruger Effect Bias
type: Class
subclass of: Cognitive Bias
definition: A cognitive bias in which people with low ability in an area overestimate that ability. Often measured by comparing self-assessment with objective performance.
has exact synonym: Dunning-Kruger Effect
in subset: BiasSubset

Axiom
annotated source: Dunning-Kruger Effect Bias
annotated property: definition
annotated target: A cognitive bias in which people with low ability in an area overestimate that ability. Often measured by comparing self-assessment with objective performance.
has db xref: NIST.SP.1270

Dynamic Conditional Correlation
type: Class
subclass of: Model
definition: A model that allows for time-varying correlations between different time series, used in financial econometrics to model and forecast covariances.
has exact synonym: DCC
in subset: ModelSubset

ELU Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that is x if x > 0 and alpha * (exp(x) - 1) if x < 0 where alpha controls the value to which an ELU saturates for negative net inputs.
has exact synonym: ELU
has exact synonym: Exponential Linear Unit
in subset: ActivationFunctionSubset
in subset: FunctionSubset
comment: The exponential linear unit (ELU) with alpha > 0 is: x if x > 0 and alpha * (exp(x) - 1) if x < 0 The ELU hyperparameter alpha controls the value to which an ELU saturates for negative net inputs. ELUs diminish the vanishing gradient effect. ELUs have negative values which pushes the mean of the activations closer to zero. Mean activations that are closer to zero enable faster Learning as they bring the gradient closer to the natural gradient. ELUs saturate to a negative value when the argument gets smaller. Saturation means a small derivative which decreases the variation and the information that is propagated to the next layer.

Axiom
annotated source: ELU Function
annotated property: definition
annotated target: A mathematical function that is x if x > 0 and alpha * (exp(x) - 1) if x < 0 where alpha controls the value to which an ELU saturates for negative net inputs.
has db xref: elu

ELU Layer
type: Class
subclass of: Activation Layer
definition: An activation layer that applies the Exponential Linear Unit (ELU) function element-wise.
in subset: LayerSubset
comment: Exponential Linear Unit.

Axiom
annotated source: ELU Layer
annotated property: definition
annotated target: An activation layer that applies the Exponential Linear Unit (ELU) function element-wise.
has db xref: ELU

Echo State Network
type: Class
subclass of: Recurrent Neural Network
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
subclass of: Restriction
  on property: has part
  some values from: Recurrent Layer
definition: A recurrent neural network with a recurrent hidden layer and sparsely connected hidden neurons that learns output weights to produce temporal patterns.
has exact synonym: ESN
in subset: NetworkSubset
comment: Layers: Input, Recurrent, Output

Axiom
annotated source: Echo State Network
annotated property: definition
annotated target: A recurrent neural network with a recurrent hidden layer and sparsely connected hidden neurons that learns output weights to produce temporal patterns.
has db xref: Echo state network

Ecological Fallacy Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias occurring when an inference about an individual is made based on their group membership.
has exact synonym: Ecological Fallacy
in subset: BiasSubset

Axiom
annotated source: Ecological Fallacy Bias
annotated property: definition
annotated target: A selection and sampling bias occurring when an inference about an individual is made based on their group membership.
has db xref: NIST.SP.1270

Embedding Layer
type: Class
subclass of: Layer
definition: A layer that turns positive integers (indexes) into dense vectors of fixed size.
in subset: LayerSubset

Axiom
annotated source: Embedding Layer
annotated property: definition
annotated target: A layer that turns positive integers (indexes) into dense vectors of fixed size.
has db xref: Embedding

Embodied LLM
type: Class
subclass of: Large Language Model
definition: A large language model that integrates language with other modalities like vision audio and robotics to enable grounded language understanding in real-world environments.
has exact synonym: Embodied Large Language Model
has related synonym: multimodal grounding
in subset: ModelSubset
comment: An embodied LLM integrates language with other modalities like vision, audio, and robotics to enable grounded language understanding in real-world environments.

Emergent Bias
type: Class
subclass of: Use And Interpretation Bias
definition: A use and interpretation bias resulting from the use and reliance on algorithms across new or unanticipated contexts.
in subset: BiasSubset

Axiom
annotated source: Emergent Bias
annotated property: definition
annotated target: A use and interpretation bias resulting from the use and reliance on algorithms across new or unanticipated contexts.
has db xref: NIST.SP.1270

Encoder-Decoder LLM
type: Class
subclass of: Large Language Model
definition: A large language model introduced in the "Attention Is All You Need" paper. The encoder processes the input sequence to generate a hidden representation summarizing the input information, while the decoder uses this hidden representation to generate the desired output sequence.
in subset: ModelSubset

Axiom
annotated source: Encoder-Decoder LLM
annotated property: definition
annotated target: A large language model introduced in the "Attention Is All You Need" paper. The encoder processes the input sequence to generate a hidden representation summarizing the input information, while the decoder uses this hidden representation to generate the desired output sequence.
has db xref: https://www.practicalai.io/understanding-transformer-model-architectures/

Encoder LLM
type: Class
subclass of: Large Language Model
definition: A large language model that uses an encoder-only architecture to encode the input sequence into a fixed-length representation which is then used as input to a classifier or regressor for prediction.
in subset: ModelSubset
comment: An encoder-only architecture that encodes the input sequence into a fixed-length representation, which is then used as input to a classifier or regressor for prediction. The model has a pre-trained general-purpose encoder that requires fine-tuning for specific tasks.

Axiom
annotated source: Encoder LLM
annotated property: definition
annotated target: A large language model that uses an encoder-only architecture to encode the input sequence into a fixed-length representation which is then used as input to a classifier or regressor for prediction.
has db xref: https://www.practicalai.io/understanding-transformer-model-architectures/

Energy-Based LLM
type: Class
subclass of: Large Language Model
definition: A large language model which models the explicit probability density over token sequences using an energy function, rather than an autoregressive factorization. This can improve modeling of long-range dependencies and global coherence.
has exact synonym: Energy-Based Large Language Model
has related synonym: energy scoring
has related synonym: explicit density modeling
in subset: ModelSubset

Ensemble Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that use multiple learning algorithms to achieve better predictive performance than any of the constituent algorithms alone.
in subset: MachineLearningSubset

Axiom
annotated source: Ensemble Learning
annotated property: definition
annotated target: A machine learning task focused on methods that use multiple learning algorithms to achieve better predictive performance than any of the constituent algorithms alone.
has db xref: Ensemble learning

Error Propagation Bias
type: Class
subclass of: Processing Bias
definition: A processing bias characterized by the effect of variables' uncertainties (or errors more specifically random errors) on the uncertainty of a function based on them.
has exact synonym: Error Propagation
in subset: BiasSubset

Axiom
annotated source: Error Propagation Bias
annotated property: definition
annotated target: A processing bias characterized by the effect of variables' uncertainties (or errors more specifically random errors) on the uncertainty of a function based on them.
has db xref: NIST.SP.1270

Ethical LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained to uphold certain ethical principles values or rules in its language generation to increase safety and trustworthiness.
has exact synonym: Ethical Large Language Model
has related synonym: constituitional AI
has related synonym: value alignment
in subset: ModelSubset

Evaluation Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias arising when testing populations do not equally represent user populations or when inappropriate performance metrics are used.
in subset: BiasSubset

Axiom
annotated source: Evaluation Bias
annotated property: definition
annotated target: A selection and sampling bias arising when testing populations do not equally represent user populations or when inappropriate performance metrics are used.
has db xref: NIST.SP.1270

Evolutionary LLM
type: Class
subclass of: Large Language Model
definition: A large language model that applies principles of evolutionary computation to optimize its structure and parameters evolving over time to improve performance.
has exact synonym: Evolutionary Language Model
has related synonym: evolutionary algorithms
has related synonym: genetic programming
in subset: ModelSubset

Exclusion Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias occurring when specific groups of user populations are excluded from testing and analysis.
in subset: BiasSubset

Axiom
annotated source: Exclusion Bias
annotated property: definition
annotated target: A selection and sampling bias occurring when specific groups of user populations are excluded from testing and analysis.
has db xref: NIST.SP.1270

Explainable LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is designed to provide insights into its decision-making process making it easier for users to understand and trust the model's outputs by incorporating mechanisms for interpreting and explaining its predictions in human-understandable terms.
has exact synonym: Explainable Language Model
has exact synonym: XAI LLM
has related synonym: interpretability
has related synonym: model understanding
in subset: ModelSubset

Exponential Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function denoted by f(x)=exp or e^{x}.
in subset: FunctionSubset
comment: The exponential function is a mathematical function denoted by f(x)=exp or e^{x}.

Axiom
annotated source: Exponential Function
annotated property: definition
annotated target: A mathematical function denoted by f(x)=exp or e^{x}.
has db xref: exponential

Exponential Smoothing State Space Model
type: Class
subclass of: Model
definition: A model that combines exponential smoothing with state space modeling, allowing for the inclusion of both trend and seasonal components. Used in forecasting.
has exact synonym: ETS
in subset: ModelSubset

Extreme Learning Machine
type: Class
subclass of: Feedback Network
definition: A feedback network with randomly assigned hidden nodes that are not updated during training.
has exact synonym: ELM
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: Extreme Learning Machine
annotated property: definition
annotated target: A feedback network with randomly assigned hidden nodes that are not updated during training.
has db xref: Extreme Learning machine

Factored Language Model
type: Class
subclass of: Language Model
definition: A language model that views each word as a vector of multiple factors such as part-of-speech morphology and semantics to improve language modeling.
has exact synonym: Factorized Language Model
in subset: ModelSubset

Axiom
annotated source: Factored Language Model
annotated property: definition
annotated target: A language model that views each word as a vector of multiple factors such as part-of-speech morphology and semantics to improve language modeling.
has db xref: Factored language model

Factorized LLM
type: Class
subclass of: Large Language Model
definition: A large language model that decomposes the full language modeling task into multiple sub-components or experts that each focus on a subset of the information enabling more efficient scaling.
has exact synonym: Factorized Large Language Model
has exact synonym: Factorized Learning Assisted with Large Language Model
has related synonym: Conditional masking
has related synonym: Product of experts
in subset: ModelSubset

Axiom
annotated source: Factorized LLM
annotated property: definition
annotated target: A large language model that decomposes the full language modeling task into multiple sub-components or experts that each focus on a subset of the information enabling more efficient scaling.
has db xref: arXiv.2403.12556

Feature Extraction
type: Class
subclass of: DataEnhancement
definition: A data enhancement that transforms raw data into a set of measurable characteristics that can be used as input for machine learning algorithms, enhancing the ability to make accurate predictions.
has exact synonym: Attribute Extraction
has exact synonym: Feature Isolation
has related synonym: Semantic embeddings
has related synonym: Syntactic information
in subset: PreprocessingSubset

Federated LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained in a decentralized manner across multiple devices or silos without directly sharing private data enabling collaborative training while preserving data privacy and security.
has exact synonym: Federated Large Language Model
has related synonym: decentralized training
has related synonym: privacy-preserving
in subset: ModelSubset

Federated Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network trained across decentralized edge devices or servers holding local data samples without exchanging them.
in subset: NetworkSubset
comment: Training an algorithm across multiple decentralized edge devices or servers holding local data samples without exchanging them.

Axiom
annotated source: Federated Learning
annotated property: definition
annotated target: A deep neural network trained across decentralized edge devices or servers holding local data samples without exchanging them.
has db xref: Federated learning

Feedback Loop Bias
type: Class
subclass of: Use And Interpretation Bias
definition: A use and interpretation bias occurring when an algorithm learns from user behavior and feeds that behavior back into the model.
in subset: BiasSubset

Axiom
annotated source: Feedback Loop Bias
annotated property: definition
annotated target: A use and interpretation bias occurring when an algorithm learns from user behavior and feeds that behavior back into the model.
has db xref: NIST.SP.1270

Feedback Network
type: Class
subclass of: Artificial Neural Network
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: An artificial neural network that refines its representations iteratively based on feedback from previous outputs.
has exact synonym: FBN
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output, Hidden

Fixed Effects Model
type: Class
subclass of: Regression Analysis
definition: A regression analysis in which the model parameters are fixed or non-random quantities.
has exact synonym: FEM
in subset: MachineLearningSubset

Axiom
annotated source: Fixed Effects Model
annotated property: definition
annotated target: A regression analysis in which the model parameters are fixed or non-random quantities.
has db xref: Fixed effects model

Flatten Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that flattens the input
in subset: LayerSubset
comment: Flattens the input. Does not affect the batch size.

Axiom
annotated source: Flatten Layer
annotated property: definition
annotated target: A reshaping layer that flattens the input
has db xref: Flatten

FractionalMaxPool2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 2D fractional max pooling over an input signal composed of several input planes.
has exact synonym: FractionalMaxPool2D
in subset: LayerSubset

Axiom
annotated source: FractionalMaxPool2D Layer
annotated property: definition
annotated target: A pooling layer that applies a 2D fractional max pooling over an input signal composed of several input planes.
has db xref: pooling-layers

FractionalMaxPool3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies a 3D fractional max pooling over an input signal composed of several input planes.
has exact synonym: FractionalMaxPool3D
in subset: LayerSubset

Axiom
annotated source: FractionalMaxPool3D Layer
annotated property: definition
annotated target: A pooling layer that applies a 3D fractional max pooling over an input signal composed of several input planes.
has db xref: pooling-layers

Funding Bias
type: Class
subclass of: Group Bias
definition: A group bias arising when biased results are reported to support or satisfy the funding agency or financial supporter of a research study.
in subset: BiasSubset

Axiom
annotated source: Funding Bias
annotated property: definition
annotated target: A group bias arising when biased results are reported to support or satisfy the funding agency or financial supporter of a research study.
has db xref: NIST.SP.1270

GELU Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that computes x * P(X <= x) where P(X) ~ N(0 1) weighting inputs by their value rather than gating inputs by their sign as in ReLU.
has exact synonym: GELU
has exact synonym: Gaussian Error Linear Unit
in subset: FunctionSubset
comment: Gaussian error linear unit (GELU) computes x * P(X <= x), where P(X) ~ N(0, 1). The (GELU) nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLU.

Axiom
annotated source: GELU Function
annotated property: definition
annotated target: A mathematical function that computes x * P(X <= x) where P(X) ~ N(0 1) weighting inputs by their value rather than gating inputs by their sign as in ReLU.
has db xref: gelu

GRUCell Layer
type: Class
subclass of: Layer
definition: A layer that processes one step within the whole time sequence input for a GRU layer.
in subset: LayerSubset
comment: Cell class for the GRU layer. This class processes one step within the whole time sequence input, whereas tf.keras.layer.GRU processes the whole sequence.

Axiom
annotated source: GRUCell Layer
annotated property: definition
annotated target: A layer that processes one step within the whole time sequence input for a GRU layer.
has db xref: GRUCell

GRU Layer
type: Class
subclass of: Recurrent Layer
definition: A recurrent layer that implements the Gated Recurrent Unit architecture.
in subset: LayerSubset
comment: Gated Recurrent Unit - Cho et al. 2014. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation. The requirements to use the cuDNN implementation are: activation == tanh, recurrent_activation == sigmoid, recurrent_dropout == 0, unroll is False, use_bias is True, reset_after is True. Inputs, if use masking, are strictly right-padded. Eager execution is enabled in the outermost context. There are two variants of the GRU implementation. The default one is based on v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original and has the order reversed. The second variant is compatible with CuDNNGRU (GPU-only) and allows inference on CPU. Thus it has separate biases for kernel and recurrent_kernel. To use this variant, set reset_after=True and recurrent_activation='sigmoid'.

Axiom
annotated source: GRU Layer
annotated property: definition
annotated target: A recurrent layer that implements the Gated Recurrent Unit architecture.
has db xref: GRU

Gated Recurrent Unit
type: Class
subclass of: Long Short Term Memory
definition: A long short-term memory network that is a gating mechanism in recurrent neural networks similar to LSTMs but with fewer parameters and no output gate.
has exact synonym: GRU
in subset: NetworkSubset
comment: Layers: Input, Memory Cell, Output

Axiom
annotated source: Gated Recurrent Unit
annotated property: definition
annotated target: A long short-term memory network that is a gating mechanism in recurrent neural networks similar to LSTMs but with fewer parameters and no output gate.
has db xref: Gated recurrent unit

GaussianDropout Layer
type: Class
subclass of: Regularization Layer
definition: A regularization layer that applies multiplicative 1-centered Gaussian noise.
in subset: LayerSubset
comment: Apply multiplicative 1-centered Gaussian noise. As it is a regularization layer, it is only active at training time.

Axiom
annotated source: GaussianDropout Layer
annotated property: definition
annotated target: A regularization layer that applies multiplicative 1-centered Gaussian noise.
has db xref: GaussianDropout

GaussianNoise Layer
type: Class
subclass of: Regularization Layer
definition: A regularization layer that applies additive zero-centered Gaussian noise.
in subset: LayerSubset
comment: Apply additive zero-centered Gaussian noise. This is useful to mitigate overfitting (you could see it as a form of random data augmentation). Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs. As it is a regularization layer, it is only active at training time.

Axiom
annotated source: GaussianNoise Layer
annotated property: definition
annotated target: A regularization layer that applies additive zero-centered Gaussian noise.
has db xref: GaussianNoise

Generalized Autoregressive Conditional Heteroskedasticity
type: Class
subclass of: Model
definition: A model that incorporates lagged conditional variances, allowing for more flexibility in modeling time-varying volatility.
has exact synonym: GARCH
in subset: ModelSubset

Generalized Few-shot Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that learns novel classes from few samples per class, preventing catastrophic forgetting of base classes and ensuring classifier calibration.
has exact synonym: GFSL
in subset: NetworkSubset

Axiom
annotated source: Generalized Few-shot Learning
annotated property: definition
annotated target: A deep neural network that learns novel classes from few samples per class, preventing catastrophic forgetting of base classes and ensuring classifier calibration.
has db xref: review

Generalized Linear Model
type: Class
subclass of: Regression Analysis
definition: A regression analysis that relates the linear model to the response variable via a link function and allowing the variance of each measurement to be a function of its predicted value.
has exact synonym: GLM
in subset: MachineLearningSubset

Axiom
annotated source: Generalized Linear Model
annotated property: definition
annotated target: A regression analysis that relates the linear model to the response variable via a link function and allowing the variance of each measurement to be a function of its predicted value.
has db xref: Generalized linear model

Generative Adversarial Network
type: Class
subclass of: Unsupervised Pretrained Network
subclass of: Restriction
  on property: has part
  some values from: Backfed Input Layer
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Matched Input-Output Layer
definition: An unsupervised pretrained network framework where two neural networks contest in a game to generate new data with the same statistics as the training set.
has exact synonym: GAN
in subset: NetworkSubset
comment: Layers: Backfed Input, Hidden, Matched Output-Input, Hidden, Matched Output-Input

Axiom
annotated source: Generative Adversarial Network
annotated property: definition
annotated target: An unsupervised pretrained network framework where two neural networks contest in a game to generate new data with the same statistics as the training set.
has db xref: Generative adversarial network

Generative Adversarial Network-Augmented LLM
type: Class
subclass of: Large Language Model
definition: A large language model which incorporates a generative adversarial network (GAN) into its training process, using a discriminator network to provide a signal for generating more realistic and coherent text. This adversarial training can improve the quality and diversity of generated text.
has exact synonym: GAN-Large Language Model
has exact synonym: Generative Adversarial Network-Augmented Large Language Model
has related synonym: adversarial training
has related synonym: text generation
in subset: ModelSubset

Generative Commonsense LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained to understand and model basic physics causality and common sense about how the real world works.
has exact synonym: Generative Commonsense Large Language Model
has exact synonym: World Model
has related synonym: causal modeling
has related synonym: physical reasoning
in subset: ModelSubset

Axiom
annotated source: Generative Commonsense LLM
annotated property: definition
annotated target: A large language model that is trained to understand and model basic physics causality and common sense about how the real world works.
has db xref: https://arxiv.org/abs/2306.12672

Generative Language Interface
type: Class
subclass of: Language Model
definition: A language model that enables users to engage in an interactive dialogue with an LLM providing feedback to guide and refine the generated outputs iteratively.
has related synonym: Interactive generation
in subset: ModelSubset

GlobalAveragePooling1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs global average pooling operation for temporal data.
has exact synonym: GlobalAvgPool1D
in subset: LayerSubset
comment: Global average pooling operation for temporal data.

Axiom
annotated source: GlobalAveragePooling1D Layer
annotated property: definition
annotated target: A pooling layer that performs global average pooling operation for temporal data.
has db xref: GlobalAveragePooling1D

GlobalAveragePooling2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs global average pooling operation for spatial data.
has exact synonym: GlobalAvgPool2D
in subset: LayerSubset
comment: Global average pooling operation for spatial data.

Axiom
annotated source: GlobalAveragePooling2D Layer
annotated property: definition
annotated target: A pooling layer that performs global average pooling operation for spatial data.
has db xref: GlobalAveragePooling2D

GlobalAveragePooling3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs global average pooling operation for 3D data.
has exact synonym: GlobalAvgPool3D
in subset: LayerSubset
comment: Global Average pooling operation for 3D data.

Axiom
annotated source: GlobalAveragePooling3D Layer
annotated property: definition
annotated target: A pooling layer that performs global average pooling operation for 3D data.
has db xref: GlobalAveragePooling3D

GlobalMaxPooling1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs global max pooling operation for temporal data.
has exact synonym: GlobalMaxPool1D
in subset: LayerSubset
comment: Global max pooling operation for 1D temporal data.

Axiom
annotated source: GlobalMaxPooling1D Layer
annotated property: definition
annotated target: A pooling layer that performs global max pooling operation for temporal data.
has db xref: GlobalMaxPool1D

GlobalMaxPooling2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs global max pooling operation for spatial data.
has exact synonym: GlobalMaxPool2D
in subset: LayerSubset
comment: Global max pooling operation for spatial data.

Axiom
annotated source: GlobalMaxPooling2D Layer
annotated property: definition
annotated target: A pooling layer that performs global max pooling operation for spatial data.
has db xref: GlobalMaxPool2D

GlobalMaxPooling3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs global max pooling operation for 3D data.
has exact synonym: GlobalMaxPool3D
in subset: LayerSubset
comment: Global Max pooling operation for 3D data.

Axiom
annotated source: GlobalMaxPooling3D Layer
annotated property: definition
annotated target: A pooling layer that performs global max pooling operation for 3D data.
has db xref: GlobalMaxPool3D

Graph Convolutional Network
type: Class
subclass of: Deep Neural Network
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: A deep neural network that operates directly on graph structures utilizing structural information.
has exact synonym: GCN
in subset: NetworkSubset
comment: Layers: Input, Hidden, Hidden, Output

Axiom
annotated source: Graph Convolutional Network
annotated property: definition
annotated target: A deep neural network that operates directly on graph structures utilizing structural information.
has db xref: https://arxiv.org/abs/1609.02907

Graph Convolutional Policy Network
type: Class
subclass of: Graph Convolutional Network
subclass of: Restriction
  on property: has part
  some values from: Policy Layer
definition: A graph convolutional network that generates goal-directed graphs using reinforcement learning and optimizing for rewards and adversarial loss.
has exact synonym: GPCN
in subset: NetworkSubset
comment: Layers: Input, Hidden, Hidden, Policy, Output

Axiom
annotated source: Graph Convolutional Policy Network
annotated property: definition
annotated target: A graph convolutional network that generates goal-directed graphs using reinforcement learning and optimizing for rewards and adversarial loss.
has db xref: https://arxiv.org/abs/1806.02473

Graph Language Model
type: Class
subclass of: Language Model
definition: A language model that operates over structured inputs or outputs represented as graphs enabling reasoning over explicit relational knowledge representations during language tasks.
has exact synonym: Graph LM
has related synonym: Structured representations
in subset: ModelSubset

Axiom
annotated source: Graph Language Model
annotated property: definition
annotated target: A language model that operates over structured inputs or outputs represented as graphs enabling reasoning over explicit relational knowledge representations during language tasks.
has db xref: https://arxiv.org/abs/2401.07105

Group Bias
type: Class
subclass of: Systemic Bias
definition: A systemic bias characterized by favoring members of one's in-group over out-group members expressed in evaluation resource allocation and other ways.
has exact synonym: In-group Favoritism
has exact synonym: In-group bias
has exact synonym: In-group preference
has exact synonym: In-group–out-group Bias
has exact synonym: Intergroup bias
in subset: BiasSubset
comment: Favoring members of one's in-group over out-group members, expressed in evaluation, resource allocation, and other ways.

Axiom
annotated source: Group Bias
annotated property: definition
annotated target: A systemic bias characterized by favoring members of one's in-group over out-group members expressed in evaluation resource allocation and other ways.
has db xref: In-group favoritism

GroupNorm Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that applies Group Normalization over a mini-batch of inputs.
has exact synonym: GroupNorm
in subset: LayerSubset
comment: Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization

Axiom
annotated source: GroupNorm Layer
annotated property: definition
annotated target: A normalization layer that applies Group Normalization over a mini-batch of inputs.
has db xref: normalization-layers

Groupthink Bias
type: Class
subclass of: Group Bias
definition: A group bias in which people in a group make non-optimal decisions due to a desire to conform or fear of dissent.
has exact synonym: Groupthink
in subset: BiasSubset

Axiom
annotated source: Groupthink Bias
annotated property: definition
annotated target: A group bias in which people in a group make non-optimal decisions due to a desire to conform or fear of dissent.
has db xref: NIST.SP.1270

Hard Sigmoid Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that is a faster approximation of the sigmoid activation using a piecewise linear approximation.
in subset: FunctionSubset
comment: A faster approximation of the sigmoid activation. Piecewise linear approximation of the sigmoid function. Ref: 'https://en.wikipedia.org/wiki/Hard_sigmoid'

Axiom
annotated source: Hard Sigmoid Function
annotated property: definition
annotated target: A mathematical function that is a faster approximation of the sigmoid activation using a piecewise linear approximation.
has db xref: hard sigmoid

Hashing Layer
type: Class
subclass of: Categorical Features Preprocessing Layer
definition: A categorical features preprocessing layer which hashes and bins categorical features.
in subset: LayerSubset
comment: A preprocessing layer which hashes and bins categorical features. This layer transforms categorical inputs to hashed output. It element-wise converts a ints or strings to ints in a fixed range. The stable hash function uses tensorflow::ops::Fingerprint to produce the same output consistently across all platforms. This layer uses FarmHash64 by default, which provides a consistent hashed output across different platforms and is stable across invocations, regardless of device and context, by mixing the input bits thoroughly. If you want to obfuscate the hashed output, you can also pass a random salt argument in the constructor. In that case, the layer will use the SipHash64 hash function, with the salt value serving as additional input to the hash function.

Axiom
annotated source: Hashing Layer
annotated property: definition
annotated target: A categorical features preprocessing layer which hashes and bins categorical features.
has db xref: Hashing

Hidden Layer
type: Class
subclass of: Layer
definition: A layer located between the input and output that performs nonlinear transformations of the inputs entered into the network.
in subset: LayerSubset
comment: A hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network. Hidden layers vary depending on the function of the neural network, and similarly, the layers may vary depending on their associated weights.

Axiom
annotated source: Hidden Layer
annotated property: definition
annotated target: A layer located between the input and output that performs nonlinear transformations of the inputs entered into the network.
has db xref: hidden-layer-machine-Learning

Hierarchical Classification
type: Class
subclass of: Classification
definition: A classification focused on methods that group things according to a hierarchy.
in subset: MachineLearningSubset
comment: Methods that group things according to a hierarchy.

Axiom
annotated source: Hierarchical Classification
annotated property: definition
annotated target: A classification focused on methods that group things according to a hierarchy.
has db xref: Hierarchical classification

Hierarchical Clustering
type: Class
subclass of: Clustering
definition: A clustering that builds a hierarchy of clusters.
has exact synonym: HCL
in subset: MachineLearningSubset
comment: Methods that build a hierarchy of clusters.

Axiom
annotated source: Hierarchical Clustering
annotated property: definition
annotated target: A clustering that builds a hierarchy of clusters.
has db xref: Hierarchical clustering

Hierarchical Language Model
type: Class
subclass of: Language Model
definition: A language model that represents language at multiple levels of granularity learning hierarchical representations that capture both low-level patterns and high-level abstractions.
has exact synonym: Hierarchical LM
has related synonym: multi-scale representations
in subset: ModelSubset

Axiom
annotated source: Hierarchical Language Model
annotated property: definition
annotated target: A language model that represents language at multiple levels of granularity learning hierarchical representations that capture both low-level patterns and high-level abstractions.
has db xref: j.ipm.2024.103698

Historical Bias
type: Class
subclass of: Bias
definition: A bias characterized by long-standing biases encoded in society over time distinct from biases in historical description or interpretation.
in subset: BiasSubset
comment: Long-standing biases encoded in society over time, distinct from biases in historical description or the interpretation of history, such as viewing the larger world from a Western or European perspective.

Axiom
annotated source: Historical Bias
annotated property: definition
annotated target: A bias characterized by long-standing biases encoded in society over time distinct from biases in historical description or interpretation.
has db xref: NIST.SP.1270

Hopfield Network
type: Class
subclass of: Symmetrically Connected Network
subclass of: Restriction
  on property: has part
  some values from: Backfed Input Layer
definition: A symmetrically connected network that is a type of recurrent artificial neural network serving as a content-addressable memory system.
has exact synonym: HN
has exact synonym: Ising model of a neural network
has exact synonym: Ising–Lenz–Little model
in subset: NetworkSubset
comment: Layers: Backfed input

Axiom
annotated source: Hopfield Network
annotated property: definition
annotated target: A symmetrically connected network that is a type of recurrent artificial neural network serving as a content-addressable memory system.
has db xref: Hopfield network

Hostile Attribution Bias
type: Class
subclass of: Use And Interpretation Bias
definition: A use and interpretation bias where individuals perceive benign or ambiguous behaviors as hostile.
in subset: BiasSubset
comment: Bias where individuals perceive benign or ambiguous behaviors as hostile.

Axiom
annotated source: Hostile Attribution Bias
annotated property: definition
annotated target: A use and interpretation bias where individuals perceive benign or ambiguous behaviors as hostile.
has db xref: Interpretive bias

Human Bias
type: Class
subclass of: Bias
definition: A bias in human thought based on heuristic principles leading to simplified judgmental operations.
in subset: BiasSubset

Axiom
annotated source: Human Bias
annotated property: definition
annotated target: A bias in human thought based on heuristic principles leading to simplified judgmental operations.
has db xref: NIST.SP.1270

Human Reporting Bias
type: Class
subclass of: Individual Bias
definition: An individual bias that arises when users depend on automated systems as heuristic substitutes for their own information-seeking and processing efforts.
in subset: BiasSubset

Axiom
annotated source: Human Reporting Bias
annotated property: definition
annotated target: An individual bias that arises when users depend on automated systems as heuristic substitutes for their own information-seeking and processing efforts.
has db xref: NIST.SP.1270

Image Augmentation Layer
type: Class
subclass of: Layer
definition: A layer that performs image data preprocessing augmentations.
in subset: LayerSubset

Axiom
annotated source: Image Augmentation Layer
annotated property: definition
annotated target: A layer that performs image data preprocessing augmentations.
has db xref: preprocessing layers

Image Preprocessing Layer
type: Class
subclass of: Layer
definition: A layer that performs image data preprocessing operations.
in subset: LayerSubset

Axiom
annotated source: Image Preprocessing Layer
annotated property: definition
annotated target: A layer that performs image data preprocessing operations.
has db xref: preprocessing layers

Implicit Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by unconscious beliefs attitudes feelings associations or stereotypes that affect information processing decision-making and actions.
has exact synonym: Confirmatory Bias
in subset: BiasSubset
comment: Unconscious beliefs, attitudes, feelings, associations, or stereotypes that affect information processing, decision-making, and actions.

Axiom
annotated source: Implicit Bias
annotated property: definition
annotated target: An individual bias characterized by unconscious beliefs attitudes feelings associations or stereotypes that affect information processing decision-making and actions.
has db xref: NIST.SP.1270

Implicit Language Model
type: Class
subclass of: Language Model
definition: A language model that uses an energy function to score entire sequences instead of factorizing probabilities autoregressively better capturing global properties and long-range dependencies.
has exact synonym: Implicit LM
has related synonym: Energy-based models
has related synonym: Token-level scoring
in subset: ModelSubset

Axiom
annotated source: Implicit Language Model
annotated property: definition
annotated target: A language model that uses an energy function to score entire sequences instead of factorizing probabilities autoregressively better capturing global properties and long-range dependencies.
has db xref: https://arxiv.org/pdf/2303.16189

Incremenetal Few-shot Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network trained on a base set of classes and then presented with novel classes, each with few labeled examples.
has exact synonym: IFSL
in subset: NetworkSubset

Axiom
annotated source: Incremenetal Few-shot Learning
annotated property: definition
annotated target: A deep neural network trained on a base set of classes and then presented with novel classes, each with few labeled examples.
has db xref: https://arxiv.org/abs/1810.07218

Individual Bias
type: Class
subclass of: Bias
definition: A bias characterized by a persistent point of view or limited list of such points of view, applied by an individual.
in subset: BiasSubset

Axiom
annotated source: Individual Bias
annotated property: definition
annotated target: A bias characterized by a persistent point of view or limited list of such points of view, applied by an individual.
has db xref: Individual bias

Inherited Bias
type: Class
subclass of: Processing Bias
definition: A processing bias arising when machine learning applications generate inputs for other machine learning algorithms passing on any existing bias.
in subset: BiasSubset

Axiom
annotated source: Inherited Bias
annotated property: definition
annotated target: A processing bias arising when machine learning applications generate inputs for other machine learning algorithms passing on any existing bias.
has db xref: NIST.SP.1270

Input Layer
type: Class
subclass of: Layer
definition: A layer composed of artificial input neurons that brings the initial data into the system for further processing by subsequent layers.
in subset: LayerSubset
comment: The input layer of a neural network is composed of artificial input neurons, and brings the initial data into the system for further processing by subsequent layers of artificial neurons. The input layer is the very beginning of the workflow for the artificial neural network.

Axiom
annotated source: Input Layer
annotated property: definition
annotated target: A layer composed of artificial input neurons that brings the initial data into the system for further processing by subsequent layers.
has db xref: input-layer-neural-networks

InputLayer Layer
type: Class
subclass of: Layer
definition: A layer to be used as an entry point into a Network (a graph of layers).
in subset: LayerSubset

Axiom
annotated source: InputLayer Layer
annotated property: definition
annotated target: A layer to be used as an entry point into a Network (a graph of layers).
has db xref: InputLayer

InputSpec Layer
type: Class
subclass of: Layer
definition: A layer that specifies the rank
in subset: LayerSubset
comment: Specifies the rank, dtype and shape of every input to a layer. Layers can expose (if appropriate) an input_spec attribute: an instance of InputSpec, or a nested structure of InputSpec instances (one per input tensor). These objects enable the layer to run input compatibility checks for input structure, input rank, input shape, and input dtype. A None entry in a shape is compatible with any dimension, a None shape is compatible with any shape.

Axiom
annotated source: InputSpec Layer
annotated property: definition
annotated target: A layer that specifies the rank
has db xref: InputSpec

InstanceNorm1D Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that applies Instance Normalization over a 2D (unbatched) or 3D (batched) input.
has exact synonym: InstanceNorm1D
in subset: LayerSubset
comment: Applies Instance Normalization over a 2D (unbatched) or 3D (batched) input as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.

Axiom
annotated source: InstanceNorm1D Layer
annotated property: definition
annotated target: A normalization layer that applies Instance Normalization over a 2D (unbatched) or 3D (batched) input.
has db xref: normalization-layers

InstanceNorm2D
type: Class
subclass of: Normalization Layer
definition: A normalization layer that applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension).
in subset: LayerSubset
comment: Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.

Axiom
annotated source: InstanceNorm2D
annotated property: definition
annotated target: A normalization layer that applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension).
has db xref: normalization-layers

InstanceNorm3D Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension).
has exact synonym: InstanceNorm3D
in subset: LayerSubset
comment: Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.

Axiom
annotated source: InstanceNorm3D Layer
annotated property: definition
annotated target: A normalization layer that applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension).
has db xref: normalization-layers

Institutional Bias
type: Class
subclass of: Systemic Bias
definition: A systemic bias exhibited at the level of entire institutions where practices or norms result in the favoring or disadvantaging of certain social groups.
in subset: BiasSubset
comment: Bias exhibited at the level of entire institutions, where practices or norms result in the favoring or disadvantaging of certain social groups, such as institutional racism or sexism.

Axiom
annotated source: Institutional Bias
annotated property: definition
annotated target: A systemic bias exhibited at the level of entire institutions where practices or norms result in the favoring or disadvantaging of certain social groups.
has db xref: NIST.SP.1270

Instruction-Tuned LLM
type: Class
subclass of: Large Language Model
definition: A large language model which is fine-tuned to follow natural language instructions accurately and safely, learning to map from instructions to desired model behavior in a more controlled and principled way.
has exact synonym: Instruction-Tuned Large Language Model
has related synonym: constitutional AI
has related synonym: natural language instructions
in subset: ModelSubset

IntegerLookup Layer
type: Class
subclass of: Categorical Features Preprocessing Layer
definition: A categorical features preprocessing layer that maps integer features to contiguous ranges.
in subset: LayerSubset

Axiom
annotated source: IntegerLookup Layer
annotated property: definition
annotated target: A categorical features preprocessing layer that maps integer features to contiguous ranges.
has db xref: IntegerLookup

Interpretation Bias
type: Class
subclass of: Individual Bias
definition: An individual bias where users interpret algorithmic outputs according to their internalized biases and views.
in subset: BiasSubset

Axiom
annotated source: Interpretation Bias
annotated property: definition
annotated target: An individual bias where users interpret algorithmic outputs according to their internalized biases and views.
has db xref: NIST.SP.1270

Kernel Layer
type: Class
subclass of: Layer
definition: A layer that obtains the dot product of input values or subsets of input values.
in subset: LayerSubset

K-nearest Neighbor Algorithm
type: Class
subclass of: Machine Learning Task
definition: A machine learning task that groups objects by a plurality vote of its neighbors, assigning each object to the class most common among its k nearest neighbors.
has exact synonym: K-NN
has exact synonym: KNN
in subset: MachineLearningSubset

Axiom
annotated source: K-nearest Neighbor Algorithm
annotated property: definition
annotated target: A machine learning task that groups objects by a plurality vote of its neighbors, assigning each object to the class most common among its k nearest neighbors.
has db xref: K-nearest neighbors algorithm

K-nearest Neighbor Classification Algorithm
type: Class
subclass of: Clustering
definition: A classification and clustering that classifies objects by a plurality vote of its neighbors, assigning each object to the class most common among its k nearest neighbors.
has exact synonym: K-NN Classification
has exact synonym: KNN Classification
in subset: MachineLearningSubset

Axiom
annotated source: K-nearest Neighbor Classification Algorithm
annotated property: definition
annotated target: A classification and clustering that classifies objects by a plurality vote of its neighbors, assigning each object to the class most common among its k nearest neighbors.
has db xref: K-nearest neighbors algorithm

K-nearest Neighbor Regression Algorithm
type: Class
subclass of: Regression Analysis
definition: An regression analysis that assigns the average of the values of k nearest neighbors to objects.
has exact synonym: K-NN Regression
has exact synonym: KNN Regression
in subset: MachineLearningSubset

Axiom
annotated source: K-nearest Neighbor Regression Algorithm
annotated property: definition
annotated target: An regression analysis that assigns the average of the values of k nearest neighbors to objects.
has db xref: K-nearest neighbors algorithm

Knowledge-Grounded LLM
type: Class
subclass of: Large Language Model
definition: A large language model which incorporates external knowledge sources or knowledge bases into the model architecture, enabling it to generate more factually accurate and knowledge-aware text.
has exact synonym: Knowledge-Grounded Large Language Model
has related synonym: factual grounding
has related synonym: knowledge integration
in subset: ModelSubset

Knowledge Transfer
type: Class
subclass of: TrainingStrategy
definition: A training strategy in which knowledge is passed from one entity such as a person organization or system to another facilitating learning and adaptation in the receiving entity through various methods such as teaching training or data exchange.
has exact synonym: Inductive Transfer
has related synonym: Adaptation
has related synonym: Pretrained models
in subset: TrainingStrategySubset

Axiom
annotated source: Knowledge Transfer
annotated property: definition
annotated target: A training strategy in which knowledge is passed from one entity such as a person organization or system to another facilitating learning and adaptation in the receiving entity through various methods such as teaching training or data exchange.
has db xref: j.knosys.2015.01.010

Kohonen Network
type: Class
subclass of: Network
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
definition: A network that is an unsupervised technique producing a low-dimensional representation of high-dimensional data preserving topological structure.
has exact synonym: KN
has exact synonym: SOFM
has exact synonym: SOM
has exact synonym: Self-Organizing Feature Map
has exact synonym: Self-Organizing Map
in subset: NetworkSubset
comment: Layers: Input, Hidden

Axiom
annotated source: Kohonen Network
annotated property: definition
annotated target: A network that is an unsupervised technique producing a low-dimensional representation of high-dimensional data preserving topological structure.
has db xref: Self-organizing map

LPPool1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies 1D power-average pooling over an input signal composed of several input planes.
has exact synonym: LPPool1D
in subset: LayerSubset

Axiom
annotated source: LPPool1D Layer
annotated property: definition
annotated target: A pooling layer that applies 1D power-average pooling over an input signal composed of several input planes.
has db xref: pooling-layers

LPPool2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that applies 2D power-average pooling over an input signal composed of several input planes.
has exact synonym: LPPool2D
in subset: LayerSubset

Axiom
annotated source: LPPool2D Layer
annotated property: definition
annotated target: A pooling layer that applies 2D power-average pooling over an input signal composed of several input planes.
has db xref: pooling-layers

LSTMCell Layer
type: Class
subclass of: Layer
definition: A layer that processes one step within the whole time sequence input for an LSTM layer.
in subset: LayerSubset
comment: Cell class for the LSTM layer.

Axiom
annotated source: LSTMCell Layer
annotated property: definition
annotated target: A layer that processes one step within the whole time sequence input for an LSTM layer.
has db xref: LSTMCell

LSTM Layer
type: Class
subclass of: Recurrent Layer
definition: A recurrent layer that implements the Long Short-Term Memory architecture.
in subset: LayerSubset
comment: Long Short-Term Memory layer - Hochreiter 1997. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation. The requirements to use the cuDNN implementation are: 1. activation == tanh, 2. recurrent_activation == sigmoid, 3. recurrent_dropout == 0, 4. unroll is False, 5. use_bias is True, 6. Inputs, if use masking, are strictly right-padded, 7. Eager execution is enabled in the outermost context.

Axiom
annotated source: LSTM Layer
annotated property: definition
annotated target: A recurrent layer that implements the Long Short-Term Memory architecture.
has db xref: LSTM

Lambda Layer
type: Class
subclass of: Layer
definition: A layer that wraps arbitrary expressions as a Layer object.
in subset: LayerSubset
comment: Wraps arbitrary expressions as a Layer object.

Axiom
annotated source: Lambda Layer
annotated property: definition
annotated target: A layer that wraps arbitrary expressions as a Layer object.
has db xref: Lambda

Language Interface LLM
type: Class
subclass of: Large Language Model
definition: A large language model that supports interactive semantic parsing enabling users to provide feedback and corrections to dynamically refine and update the language model.
has related synonym: Interactive learning
in subset: ModelSubset

Language Model
type: Class
subclass of: Model
definition: A model designed to predict the next word in a sequence or assign probabilities to sequences of words in natural language.
in subset: ModelSubset

Axiom
annotated source: Language Model
annotated property: definition
annotated target: A model designed to predict the next word in a sequence or assign probabilities to sequences of words in natural language.
has db xref: Language model

Large Language Model
type: Class
subclass of: Language Model
definition: A language model consisting of a neural network with many parameters (typically billions of weights or more) trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning.
has exact synonym: LLM
in subset: ModelSubset

Axiom
annotated source: Large Language Model
annotated property: definition
annotated target: A language model consisting of a neural network with many parameters (typically billions of weights or more) trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning.
has db xref: Large language model

Lasso Regression
type: Class
subclass of: Regression Analysis
definition: A regression analysis that performs both variable selection and regularization to enhance prediction accuracy and interpretability.
in subset: MachineLearningSubset

Axiom
annotated source: Lasso Regression
annotated property: definition
annotated target: A regression analysis that performs both variable selection and regularization to enhance prediction accuracy and interpretability.
has db xref: Lasso (statistics)

Layer
type: Class
subclass of: Thing
definition: A structure or network topology in a deep learning model that takes information from previous layers and passes it to the next layer.
in subset: ClassSubset

Axiom
annotated source: Layer
annotated property: definition
annotated target: A structure or network topology in a deep learning model that takes information from previous layers and passes it to the next layer.
has db xref: Layer (deep learning)

Layer Layer
type: Class
subclass of: Layer
definition: A layer that form which other layers can inherit.
in subset: LayerSubset
comment: This is the class from which all layers inherit. A layer is a callable object that takes as input one or more tensors and that outputs one or more tensors. It involves computation, defined in the call() method, and a state (weight variables). State can be created in various places, at the convenience of the subclass implementer: in __init__(); in the optional build() method, which is invoked by the first __call__() to the layer, and supplies the shape(s) of the input(s), which may not have been known at initialization time; in the first invocation of call(), with some caveats discussed below. Users will just instantiate a layer and then treat it as a callable.

Axiom
annotated source: Layer Layer
annotated property: definition
annotated target: A layer that form which other layers can inherit.
has db xref: Layer

LayerNorm Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that applies Layer Normalization over a mini-batch of inputs.
has exact synonym: LayerNorm
in subset: LayerSubset
comment: Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization

Axiom
annotated source: LayerNorm Layer
annotated property: definition
annotated target: A normalization layer that applies Layer Normalization over a mini-batch of inputs.
has db xref: normalization-layers

LayerNormalization Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that applies Layer Normalization over the inputs.
in subset: LayerSubset
comment: Layer normalization layer (Ba et al., 2016). Normalize the activations of the previous layer for each given example in a batch independently, rather than across a batch like Batch Normalization. i.e. applies a transformation that maintains the mean activation within each example close to 0 and the activation standard deviation close to 1. Given a tensor inputs, moments are calculated and normalization is performed across the axes specified in axis.

Axiom
annotated source: LayerNormalization Layer
annotated property: definition
annotated target: A normalization layer that applies Layer Normalization over the inputs.
has db xref: LayerNormalization

LazyBatchNorm1D Layer
type: Class
subclass of: BatchNormalization Layer
definition: A batch normalization layer that lazily initializes the num_features argument from the input size for 1D data.
has exact synonym: LazyBatchNorm1D
in subset: LayerSubset
comment: A torch.nn.BatchNorm1D module with lazy initialization of the num_features argument of the BatchNorm1D that is inferred from the input.size(1).

Axiom
annotated source: LazyBatchNorm1D Layer
annotated property: definition
annotated target: A batch normalization layer that lazily initializes the num_features argument from the input size for 1D data.
has db xref: normalization-layers

LazyBatchNorm2D Layer
type: Class
subclass of: BatchNormalization Layer
definition: A batch normalization layer that lazily initializes the num_features argument from the input size for 2D data.
has exact synonym: LazyBatchNorm2D
in subset: LayerSubset
comment: A torch.nn.BatchNorm2D module with lazy initialization of the num_features argument of the BatchNorm2D that is inferred from the input.size(1).

Axiom
annotated source: LazyBatchNorm2D Layer
annotated property: definition
annotated target: A batch normalization layer that lazily initializes the num_features argument from the input size for 2D data.
has db xref: normalization-layers

LazyBatchNorm3D Layer
type: Class
subclass of: BatchNormalization Layer
definition: A batch normalization layer that lazily initializes the num_features argument from the input size for 3D data.
has exact synonym: LazyBatchNorm3D
in subset: LayerSubset
comment: A torch.nn.BatchNorm3D module with lazy initialization of the num_features argument of the BatchNorm3D that is inferred from the input.size(1).

Axiom
annotated source: LazyBatchNorm3D Layer
annotated property: definition
annotated target: A batch normalization layer that lazily initializes the num_features argument from the input size for 3D data.
has db xref: normalization-layers

LazyInstanceNorm1D Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that lazily initializes the num_features argument from the input size for 1D data.
has exact synonym: LazyInstanceNorm1D
in subset: InstanceNormalizationLayerSubset
in subset: LayerSubset
comment: A torch.nn.InstanceNorm1D module with lazy initialization of the num_features argument of the InstanceNorm1D that is inferred from the input.size(1).

Axiom
annotated source: LazyInstanceNorm1D Layer
annotated property: definition
annotated target: A normalization layer that lazily initializes the num_features argument from the input size for 1D data.
has db xref: normalization-layers

LazyInstanceNorm2D Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that lazily initializes the num_features argument from the input size for 2D data.
has exact synonym: LazyInstanceNorm2D
in subset: InstanceNormalizationLayerSubset
in subset: LayerSubset
comment: A torch.nn.InstanceNorm2D module with lazy initialization of the num_features argument of the InstanceNorm2D that is inferred from the input.size(1).

Axiom
annotated source: LazyInstanceNorm2D Layer
annotated property: definition
annotated target: A normalization layer that lazily initializes the num_features argument from the input size for 2D data.
has db xref: normalization-layers

LazyInstanceNorm3D Layer
type: Class
subclass of: Normalization Layer
definition: A normalization that lazily initializes the num_features argument from the input size for 3D data.
has exact synonym: LazyInstanceNorm3D
in subset: InstanceNormalizationLayerSubset
in subset: LayerSubset
comment: A torch.nn.InstanceNorm3D module with lazy initialization of the num_features argument of the InstanceNorm3D that is inferred from the input.size(1).

Axiom
annotated source: LazyInstanceNorm3D Layer
annotated property: definition
annotated target: A normalization that lazily initializes the num_features argument from the input size for 3D data.
has db xref: normalization-layers

LeakyReLU Layer
type: Class
subclass of: Activation Layer
definition: An activation layer that applies the leaky rectified linear unit function element-wise.
in subset: LayerSubset
comment: Leaky version of a Rectified Linear Unit.

Axiom
annotated source: LeakyReLU Layer
annotated property: definition
annotated target: An activation layer that applies the leaky rectified linear unit function element-wise.
has db xref: LeakyReLU

Least-squares Analysis
type: Class
subclass of: Regression Analysis
definition: A regression analysis which approximates the solution of overdetermined systems by minimizing the sum of the squares of the residuals.
in subset: MachineLearningSubset

Axiom
annotated source: Least-squares Analysis
annotated property: definition
annotated target: A regression analysis which approximates the solution of overdetermined systems by minimizing the sum of the squares of the residuals.
has db xref: Least squares

Lifelong Learning LLM
type: Class
subclass of: Large Language Model
definition: A large language model that continually acquires new knowledge over time without forgetting previously learned information maintaining a balance between plasticity and stability.
has exact synonym: Continual Learning LLM
has exact synonym: Forever Learning
has related synonym: Catastrophic forgetting
has related synonym: Plasticity-Stability balance
in subset: ModelSubset

Linear Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that has the form f(x) = a + bx.
in subset: ActivationFunctionSubset
in subset: FunctionSubset

Axiom
annotated source: Linear Function
annotated property: definition
annotated target: A mathematical function that has the form f(x) = a + bx.
has db xref: linear

Linear Regression
type: Class
subclass of: Regression Analysis
definition: A regression analysis that is a linear approach for modeling the relationship between a scalar response and one or more explanatory variables.
in subset: MachineLearningSubset

Axiom
annotated source: Linear Regression
annotated property: definition
annotated target: A regression analysis that is a linear approach for modeling the relationship between a scalar response and one or more explanatory variables.
has db xref: Linear regression

Linking Bias
type: Class
subclass of: Use And Interpretation Bias
definition: A use and interpretation bias arising when network attributes obtained from user connections activities or interactions misrepresent true user behavior.
in subset: BiasSubset
comment: Bias arising when network attributes obtained from user connections, activities, or interactions misrepresent true user behavior.

Axiom
annotated source: Linking Bias
annotated property: definition
annotated target: A use and interpretation bias arising when network attributes obtained from user connections activities or interactions misrepresent true user behavior.
has db xref: NIST.SP.1270

Liquid State Machine Network
type: Class
subclass of: Network
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
subclass of: Restriction
  on property: has part
  some values from: Spiking Hidden Layer
definition: A network that is a type of reservoir computer turning time-varying input into spatio-temporal activation patterns.
has exact synonym: LSM
in subset: NetworkSubset
comment: Layers: Input, Spiking Hidden, Output

Axiom
annotated source: Liquid State Machine Network
annotated property: definition
annotated target: A network that is a type of reservoir computer turning time-varying input into spatio-temporal activation patterns.
has db xref: Liquid state machine

LocalResponseNorm Layer
type: Class
subclass of: Normalization Layer
definition: A normalization layer that applies local response normalization over an input signal composed of several input planes.
has exact synonym: LocalResponseNorm
in subset: LayerSubset
comment: Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension.

Axiom
annotated source: LocalResponseNorm Layer
annotated property: definition
annotated target: A normalization layer that applies local response normalization over an input signal composed of several input planes.
has db xref: normalization-layers

LocallyConnected1D Layer
type: Class
subclass of: Locally-connected Layer
definition: A locally-connected layer for 1D inputs where each patch of the input is convolved with a different set of filters.
in subset: LayerSubset
comment: Locally-connected layer for 1D inputs. The LocallyConnected1D layer works similarly to the Conv1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

Axiom
annotated source: LocallyConnected1D Layer
annotated property: definition
annotated target: A locally-connected layer for 1D inputs where each patch of the input is convolved with a different set of filters.
has db xref: LocallyConnected1D

LocallyConnected2D Layer
type: Class
subclass of: Locally-connected Layer
definition: A locally-connected layer for 2D inputs where each patch of the input is convolved with a different set of filters.
in subset: LayerSubset
comment: Locally-connected layer for 2D inputs. The LocallyConnected2D layer works similarly to the Conv2D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

Axiom
annotated source: LocallyConnected2D Layer
annotated property: definition
annotated target: A locally-connected layer for 2D inputs where each patch of the input is convolved with a different set of filters.
has db xref: LocallyConnected2D

Locally-connected Layer
type: Class
subclass of: Layer
definition: A layer that works similarly to the Convolution1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.
in subset: LayerSubset

Axiom
annotated source: Locally-connected Layer
annotated property: definition
annotated target: A layer that works similarly to the Convolution1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.
has db xref: local

Logistic Regression
type: Class
subclass of: Regression Analysis
definition: A regression analysis that estimates the probability of an event occurring by modeling the log-odds of the event as a linear combination of one or more independent variables.
in subset: MachineLearningSubset

Axiom
annotated source: Logistic Regression
annotated property: definition
annotated target: A regression analysis that estimates the probability of an event occurring by modeling the log-odds of the event as a linear combination of one or more independent variables.
has db xref: Logistic regression

Long Short Term Memory
type: Class
subclass of: Recurrent Neural Network
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Memory Cell Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: A recurrent neural network with feedback connections that processes entire sequences of data.
has exact synonym: LSTM
in subset: NetworkSubset
comment: Layers: Input, Memory Cell, Output

Axiom
annotated source: Long Short Term Memory
annotated property: definition
annotated target: A recurrent neural network with feedback connections that processes entire sequences of data.
has db xref: Long short-term memory

Loss Of Situational Awareness Bias
type: Class
subclass of: Individual Bias
definition: An individual bias occurring when automation leads to humans being unaware of their situation making them unprepared to assume control in cooperative systems.
in subset: BiasSubset

Axiom
annotated source: Loss Of Situational Awareness Bias
annotated property: definition
annotated target: An individual bias occurring when automation leads to humans being unaware of their situation making them unprepared to assume control in cooperative systems.
has db xref: NIST.SP.1270

Low-Resource LLM
type: Class
subclass of: Large Language Model
definition: A large language model which is optimized for performance in scenarios with limited data, computational resources, or for languages with sparse datasets.
has exact synonym: Low-Resource Language Model
has related synonym: low-resource languages
has related synonym: resource-efficient
in subset: ModelSubset

Machine Learning Task
type: Class
subclass of: Thing
definition: A process intended to build methods that learn from data.
has related synonym: Machine Learning
in subset: MachineLearningSubset

Axiom
annotated source: Machine Learning Task
annotated property: definition
annotated target: A process intended to build methods that learn from data.
has db xref: NIST.SP.1270

Manifold Learning
type: Class
subclass of: Dimensionality Reduction
definition: A dimensionality reduction method based on the assumption that observed data lie on a low-dimensional manifold embedded in a higher-dimensional space.
in subset: MachineLearningSubset

Axiom
annotated source: Manifold Learning
annotated property: definition
annotated target: A dimensionality reduction method based on the assumption that observed data lie on a low-dimensional manifold embedded in a higher-dimensional space.
has db xref: https://arxiv.org/abs/2011.01307

Markov Chain
type: Class
subclass of: Network
subclass of: Restriction
  on property: has part
  some values from: Probabilistic Hidden Layer
definition: A network that is a stochastic model describing a sequence of possible events where the probability of each event depends only on the previous event's state.
has exact synonym: MC
has exact synonym: MP
has exact synonym: Markov Process
in subset: ModelSubset
comment: Layers: Probalistic Hidden

Axiom
annotated source: Markov Chain
annotated property: definition
annotated target: A network that is a stochastic model describing a sequence of possible events where the probability of each event depends only on the previous event's state.
has db xref: Markov chain

Masked Language Model
type: Class
subclass of: Language Model
definition: A language model that is trained to predict randomly masked tokens in a sequence based on the remaining unmasked tokens allowing it to build deep bidirectional representations that can be effectively transferred to various NLP tasks via fine-tuning.
has related synonym: bidirectional encoder
has related synonym: denoising autoencoder
in subset: ModelSubset

Masking Layer
type: Class
subclass of: Layer
definition: A layer that masks a sequence by using a mask value to skip timesteps.
in subset: LayerSubset
comment: Masks a sequence by using a mask value to skip timesteps. For each timestep in the input tensor (dimension #1 in the tensor), if all values in the input tensor at that timestep are equal to mask_value, then the timestep will be masked (skipped) in all downstream layers (as long as they support masking). If any downstream layer does not support masking yet receives such an input mask, an exception will be raised.

Axiom
annotated source: Masking Layer
annotated property: definition
annotated target: A layer that masks a sequence by using a mask value to skip timesteps.
has db xref: Masking

Matched Input-Output Layer
type: Class
subclass of: Input Layer
definition: An input layer with a shape corresponding to that of the output layer.
in subset: LayerSubset

Mathematical Function
type: Class
subclass of: Thing
definition: A mathematical rule that gives the value of a dependent variable corresponding to specified values of one or more independent variables.
in subset: ClassSubset

Axiom
annotated source: Mathematical Function
annotated property: definition
annotated target: A mathematical rule that gives the value of a dependent variable corresponding to specified values of one or more independent variables.
has db xref: mathematical-function

MaxPooling1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs max pooling operation for temporal data.
has exact synonym: MaxPool1D
has exact synonym: MaxPooling1D
in subset: LayerSubset
comment: Max pooling operation for 1D temporal data. Downsamples the input representation by taking the maximum value over a spatial window of size pool_size. The window is shifted by strides. The resulting output, when using the "valid" padding option, has a shape of: output_shape = (input_shape - pool_size + 1) / strides) The resulting output shape when using the "same" padding option is: output_shape = input_shape / strides.

Axiom
annotated source: MaxPooling1D Layer
annotated property: definition
annotated target: A pooling layer that performs max pooling operation for temporal data.
has db xref: MaxPool1D

MaxPooling2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs max pooling operation for spatial data.
has exact synonym: MaxPool2D
has exact synonym: MaxPooling2D
in subset: LayerSubset
comment: Max pooling operation for 2D spatial data.

Axiom
annotated source: MaxPooling2D Layer
annotated property: definition
annotated target: A pooling layer that performs max pooling operation for spatial data.
has db xref: MaxPool2D

MaxPooling3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that performs max pooling operation for 3D data (spatial or spatio-temporal).
has exact synonym: MaxPool3D
has exact synonym: MaxPooling3D
in subset: LayerSubset
comment: Max pooling operation for 3D data (spatial or spatio-temporal). Downsamples the input along its spatial dimensions (depth, height, and width) by taking the maximum value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension.

Axiom
annotated source: MaxPooling3D Layer
annotated property: definition
annotated target: A pooling layer that performs max pooling operation for 3D data (spatial or spatio-temporal).
has db xref: MaxPool3D

MaxUnpool1D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that computes a partial inverse of MaxPool1D.
has exact synonym: MaxUnpool1D
in subset: LayerSubset
comment: Computes a partial inverse of MaxPool1D.

Axiom
annotated source: MaxUnpool1D Layer
annotated property: definition
annotated target: A pooling layer that computes a partial inverse of MaxPool1D.
has db xref: pooling-layers

MaxUnpool2D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that computes a partial inverse of MaxPool2D.
has exact synonym: MaxUnpool2D
in subset: LayerSubset
comment: Computes a partial inverse of MaxPool2D.

Axiom
annotated source: MaxUnpool2D Layer
annotated property: definition
annotated target: A pooling layer that computes a partial inverse of MaxPool2D.
has db xref: pooling-layers

MaxUnpool3D Layer
type: Class
subclass of: Pooling Layer
definition: A pooling layer that computes a partial inverse of MaxPool3D.
has exact synonym: MaxUnpool3D
in subset: LayerSubset
comment: Computes a partial inverse of MaxPool3D.

Axiom
annotated source: MaxUnpool3D Layer
annotated property: definition
annotated target: A pooling layer that computes a partial inverse of MaxPool3D.
has db xref: pooling-layers

Maximum Layer
type: Class
subclass of: Merging Layer
definition: A merging layer that computes the maximum (element-wise) of a list of inputs.
in subset: LayerSubset
comment: Layer that computes the maximum (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).

Axiom
annotated source: Maximum Layer
annotated property: definition
annotated target: A merging layer that computes the maximum (element-wise) of a list of inputs.
has db xref: Maximum

Measurement Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias arising when features and labels are proxies for desired quantities potentially leading to differential performance.
in subset: BiasSubset

Axiom
annotated source: Measurement Bias
annotated property: definition
annotated target: A selection and sampling bias arising when features and labels are proxies for desired quantities potentially leading to differential performance.
has db xref: NIST.SP.1270

Memory-Augmented LLM
type: Class
subclass of: Large Language Model
definition: A large language model which incorporates external writable and readable memory components, allowing it to store and retrieve information over long contexts.
has exact synonym: Memory-Augmented Large Language Model
has related synonym: external memory
in subset: ModelSubset

Axiom
annotated source: Memory-Augmented LLM
annotated property: definition
annotated target: A large language model which incorporates external writable and readable memory components, allowing it to store and retrieve information over long contexts.
has db xref: https://arxiv.org/abs/2306.07174

Memory Cell Layer
type: Class
subclass of: Layer
definition: A layer of cells, each with an internal state or weights.
in subset: LayerSubset

Axiom
annotated source: Memory Cell Layer
annotated property: definition
annotated target: A layer of cells, each with an internal state or weights.
has db xref: neco.1997.9.8.1735

Merging Layer
type: Class
subclass of: Layer
definition: A layer used to merge a list of inputs.
in subset: LayerSubset

Axiom
annotated source: Merging Layer
annotated property: definition
annotated target: A layer used to merge a list of inputs.
has db xref: keras merge layer.htm

Meta-Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task that automatically learns from metadata about machine learning experiments.
in subset: NetworkSubset

Axiom
annotated source: Meta-Learning
annotated property: definition
annotated target: A machine learning task that automatically learns from metadata about machine learning experiments.
has db xref: NIST.SP.1270

Meta-Learning LLM
type: Class
subclass of: Large Language Model
definition: A large language model which is trained in a way that allows it to quickly adapt to new tasks or datasets through only a few examples or fine-tuning steps, leveraging meta-learned priors about how to efficiently learn.
has exact synonym: Meta-Learning Large Language Model
has related synonym: few-shot adaptation
has related synonym: learning to learn
in subset: ModelSubset

Metric Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that learns a representation function mapping objects into an embedded space.
has exact synonym: Distance Metric Learning
in subset: NetworkSubset
comment: Learning a representation function that maps objects into an embedded space.

Axiom
annotated source: Metric Learning
annotated property: definition
annotated target: A deep neural network that learns a representation function mapping objects into an embedded space.
has db xref: metric-learning

Minimum Layer
type: Class
subclass of: Merging Layer
definition: A merging layer that computes the minimum (element-wise) of a list of inputs.
in subset: LayerSubset
comment: Layer that computes the minimum (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).

Axiom
annotated source: Minimum Layer
annotated property: definition
annotated target: A merging layer that computes the minimum (element-wise) of a list of inputs.
has db xref: Minimum

Mixture-of-Experts LLM
type: Class
subclass of: Large Language Model
definition: A large language model which dynamically selects and combines outputs from multiple expert submodels, allowing for efficient scaling by conditionally activating only a subset of model components for each input.
has exact synonym: Mixture-of-Experts Large Language Model
has exact synonym: MoE Large Language Model
has related synonym: conditional computation
has related synonym: model parallelism
in subset: ModelSubset

Axiom
annotated source: Mixture-of-Experts LLM
annotated property: definition
annotated target: A large language model which dynamically selects and combines outputs from multiple expert submodels, allowing for efficient scaling by conditionally activating only a subset of model components for each input.
has db xref: du22c.html

Mode Confusion Bias
type: Class
subclass of: Individual Bias
definition: An individual bias occurring when modal interfaces confuse human operators causing actions appropriate for a different mode but incorrect for the current situation.
in subset: BiasSubset

Axiom
annotated source: Mode Confusion Bias
annotated property: definition
annotated target: An individual bias occurring when modal interfaces confuse human operators causing actions appropriate for a different mode but incorrect for the current situation.
has db xref: NIST.SP.1270

Model
type: Class
subclass of: Thing
definition: An abstract representation of a complex system generally assembled as a set of logical mathematical or conceptual properties to simulate or understand the system's behavior.
in subset: ModelSubset

Axiom
annotated source: Model
annotated property: definition
annotated target: An abstract representation of a complex system generally assembled as a set of logical mathematical or conceptual properties to simulate or understand the system's behavior.
has db xref: Mathematical model

Model Selection Bias
type: Class
subclass of: Processing Bias
definition: A processing bias introduced when using data to select a single "best" model from many or when an explanatory variable has a weak relationship with the response variable.
in subset: BiasSubset

Axiom
annotated source: Model Selection Bias
annotated property: definition
annotated target: A processing bias introduced when using data to select a single "best" model from many or when an explanatory variable has a weak relationship with the response variable.
has db xref: NIST.SP.1270

Modular LLM
type: Class
subclass of: Modular Language Model
definition: A modular language model that consists of multiple specialized components or skills that can be dynamically composed and recombined to solve complex tasks mimicking the modular structure of human cognition.
has exact synonym: Modular Large Language Model
has related synonym: component skills
has related synonym: skill composition
in subset: ModelSubset

Axiom
annotated source: Modular LLM
annotated property: definition
annotated target: A modular language model that consists of multiple specialized components or skills that can be dynamically composed and recombined to solve complex tasks mimicking the modular structure of human cognition.
has db xref: 2302.11529v2

Modular Language Model
type: Class
subclass of: Language Model
definition: A language model that consists of multiple specialized components or skills that can be dynamically composed and recombined to solve complex tasks mimicking the modular structure of human cognition.
has exact synonym: Modular LM
in subset: ModelSubset

Axiom
annotated source: Modular Language Model
annotated property: definition
annotated target: A language model that consists of multiple specialized components or skills that can be dynamically composed and recombined to solve complex tasks mimicking the modular structure of human cognition.
has db xref: 2302.11529v2

MultiHeadAttention Layer
type: Class
subclass of: Attention Layer
definition: An attention layer that allows the model to attend to information from different representation subspaces.
in subset: LayerSubset
comment: MultiHeadAttention layer. This is an implementation of multi-headed attention as described in the paper "Attention is all you Need" (Vaswani et al., 2017). If query, key, value are the same, then this is self-attention. Each timestep in query attends to the corresponding sequence in key, and returns a fixed-width vector.This layer first projects query, key and value. These are (effectively) a list of tensors of length num_attention_heads, where the corresponding shapes are (batch_size, <query dimensions>, key_dim), (batch_size, <key/value dimensions>, key_dim), (batch_size, <key/value dimensions>, value_dim).Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor. Finally, the result tensor with the last dimension as value_dim can take an linear projection and return. When using MultiHeadAttention inside a custom Layer, the custom Layer must implement build() and call MultiHeadAttention's _build_from_signature(). This enables weights to be restored correctly when the model is loaded.

Axiom
annotated source: MultiHeadAttention Layer
annotated property: definition
annotated target: An attention layer that allows the model to attend to information from different representation subspaces.
has db xref: MultiHeadAttention

Multi-Task LLM
type: Class
subclass of: Large Language Model
definition: A large language model which is trained jointly on multiple language tasks simultaneously, learning shared representations that transfer across tasks.
has exact synonym: Multi-Task Large Language Model
has related synonym: transfer learning
in subset: ModelSubset

Multiclass Classification
type: Class
subclass of: Classification
definition: A classification focused on methods that classify instances into one of three or more classes.
has exact synonym: Multinomial Classification
in subset: MachineLearningSubset
comment: Methods that classify instances into one of three or more classes.

Axiom
annotated source: Multiclass Classification
annotated property: definition
annotated target: A classification focused on methods that classify instances into one of three or more classes.
has db xref: Multiclass classification

Multidimensional Scaling
type: Class
subclass of: Dimensionality Reduction
definition: A dimensionality reduction method that translates information about the pairwise distances among a set of objects or individuals into a configuration of points mapped into an abstract Cartesian space.
has exact synonym: MDS
in subset: MachineLearningSubset

Axiom
annotated source: Multidimensional Scaling
annotated property: definition
annotated target: A dimensionality reduction method that translates information about the pairwise distances among a set of objects or individuals into a configuration of points mapped into an abstract Cartesian space.
has db xref: Multidimensional scaling

Multilingual LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained on text from multiple languages learning shared representations that enable zero-shot or few-shot transfer to new languages.
has exact synonym: Multilingual Large Language Model
has related synonym: cross-lingual transfer
in subset: ModelSubset

Multimodal Deep Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that processes and links information using various modalities.
in subset: NetworkSubset
comment: Creating models that process and link information using various modalities.

Axiom
annotated source: Multimodal Deep Learning
annotated property: definition
annotated target: A deep neural network that processes and links information using various modalities.
has db xref: https://arxiv.org/abs/2105.11087

Multimodal Fusion LLM
type: Class
subclass of: Large Language Model
definition: A large language model that learns joint representations across different modalities like text vision and audio in an end-to-end fashion for better cross-modal understanding and generation.
has related synonym: cross-modal grounding
in subset: ModelSubset

Multimodal LLM
type: Class
subclass of: Multimodal Language Model
definition: A multimodal large language model that learns joint representations across different modalities like text vision and audio in an end-to-end fashion for better cross-modal understanding and generation.
has exact synonym: Multimodal Large Language Model
has related synonym: cross-modal grounding
in subset: ModelSubset

Axiom
annotated source: Multimodal LLM
annotated property: definition
annotated target: A multimodal large language model that learns joint representations across different modalities like text vision and audio in an end-to-end fashion for better cross-modal understanding and generation.
has db xref: https://arxiv.org/abs/2303.17580

Multimodal Language Model
type: Class
subclass of: Language Model
definition: A language model that learns joint representations across different modalities like text vision and audio in an end-to-end fashion for better cross-modal understanding and generation.
has exact synonym: Mulimodal LM
in subset: ModelSubset

Axiom
annotated source: Multimodal Language Model
annotated property: definition
annotated target: A language model that learns joint representations across different modalities like text vision and audio in an end-to-end fashion for better cross-modal understanding and generation.
has db xref: https://arxiv.org/abs/2205.12630

Multimodal Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task that uses multiple modalities of data such as text audio and images to improve learning outcomes.
in subset: NetworkSubset
comment: A type of deep learning that uses multiple modalities of data, such as text, audio, and images, to improve learning outcomes.

Axiom
annotated source: Multimodal Learning
annotated property: definition
annotated target: A machine learning task that uses multiple modalities of data such as text audio and images to improve learning outcomes.
has db xref: NIST.SP.1270

Multimodal Prompt-based Language Model
type: Class
subclass of: Multimodal Language Model
definition: A multimodal large language model which processes prompts that include multiple modalities, such as both text and images, to generate relevant responses.
in subset: ModelSubset

Axiom
annotated source: Multimodal Prompt-based Language Model
annotated property: definition
annotated target: A multimodal large language model which processes prompts that include multiple modalities, such as both text and images, to generate relevant responses.
has db xref: https://arxiv.org/abs/2210.03094

Multimodal Transformer
type: Class
subclass of: Transformer Network
definition: A transformer network that processes and relates information from different modalities such as text images and audio using a shared embedding space and attention mechanism to learn joint representations across modalities.
has related synonym: unified encoder
has related synonym: vision-language model
in subset: ModelSubset

Multiply Layer
type: Class
subclass of: Merging Layer
definition: A merging layer that multiplies (element-wise) a list of inputs.
in subset: LayerSubset
comment: Layer that multiplies (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).

Axiom
annotated source: Multiply Layer
annotated property: definition
annotated target: A merging layer that multiplies (element-wise) a list of inputs.
has db xref: Multiply

Natural Language Processing
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on the interactions between computers and human language including programming computers to process and analyze large amounts of natural language data.
has exact synonym: NLP
in subset: MachineLearningSubset

Axiom
annotated source: Natural Language Processing
annotated property: definition
annotated target: A machine learning task focused on the interactions between computers and human language including programming computers to process and analyze large amounts of natural language data.
has db xref: Natural language processing

Network
type: Class
subclass of: Thing
definition: A system of interconnected nodes or entities for communication, computation or data exchange.
in subset: ClassSubset

Neural Turing Machine Network
type: Class
subclass of: Deep Feed-Forward Network
subclass of: Restriction
  on property: has part
  some values from: Spiking Hidden Layer
definition: A deep feedforward network that combines neural network pattern matching with the algorithmic power of programmable computers.
has exact synonym: NTM
in subset: NetworkSubset
comment: Layers: Input, Hidden, Spiking Hidden, Output

Axiom
annotated source: Neural Turing Machine Network
annotated property: definition
annotated target: A deep feedforward network that combines neural network pattern matching with the algorithmic power of programmable computers.
has db xref: Neural Turing machine

Neuro-Symbolic LLM
type: Class
subclass of: Large Language Model
definition: A large language model which combines neural language modeling with symbolic reasoning components, leveraging structured knowledge representations and logical inferences to improve reasoning capabilities.
has exact synonym: Neuro-Symbolic Large Language Model
has related synonym: knowledge reasoning
has related synonym: symbolic grounding
in subset: ModelSubset

Noise Dense Layer
type: Class
subclass of: Layer
definition: A layer that is a densely-connected neural network layer with added noise for regularization.
in subset: LayerSubset
comment: Noisy dense layer that injects random noise to the weights of dense layer. Noisy dense layers are fully connected layers whose weights and biases are augmented by factorised Gaussian noise. The factorised Gaussian noise is controlled through gradient descent by a second weights layer. A NoisyDense layer implements the operation: $$ mathrm{NoisyDense}(x) = mathrm{activation}(mathrm{dot}(x, mu + (sigma cdot epsilon)) mathrm{bias}) $$ where mu is the standard weights layer, epsilon is the factorised Gaussian noise, and delta is a second weights layer which controls epsilon.

Axiom
annotated source: Noise Dense Layer
annotated property: definition
annotated target: A layer that is a densely-connected neural network layer with added noise for regularization.
has db xref: NoisyDense

Noisy Input Layer
type: Class
subclass of: Input Layer
definition: An input layer that adds noise to each value.
in subset: LayerSubset

Axiom
annotated source: Noisy Input Layer
annotated property: definition
annotated target: An input layer that adds noise to each value.
has db xref: https://doi.org/10.1109/21.155944

Normalization
type: Class
subclass of: Data Preparation
definition: A data preparation that transforms data into a standard format or scale typically to reduce redundancy and improve consistency often involving the adjustment of values measured on different scales to a common scale.
in subset: PreprocessingSubset

Normalization Layer
type: Class
subclass of: Numerical Features Preprocessing Layer
definition: A numerical features prepreprocessing layer that normalizes continuous features.
in subset: LayerSubset

Axiom
annotated source: Normalization Layer
annotated property: definition
annotated target: A numerical features prepreprocessing layer that normalizes continuous features.
has db xref: Normalization

Numerical Features Preprocessing Layer
type: Class
subclass of: Layer
definition: A layer that performs numerical data preprocessing operations.
in subset: LayerSubset

Axiom
annotated source: Numerical Features Preprocessing Layer
annotated property: definition
annotated target: A layer that performs numerical data preprocessing operations.
has db xref: preprocessing layers

One-shot Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that classified objects from one or only a few examples.
has exact synonym: OSL
in subset: NetworkSubset

Axiom
annotated source: One-shot Learning
annotated property: definition
annotated target: A deep neural network that classified objects from one or only a few examples.
has db xref: One-shot learning

Ordinal LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained to model ordinal relationships and rank outputs rather than model probability distributions over text sequences directly.
has exact synonym: Ordinal Large Language Model
has related synonym: preference modeling
has related synonym: ranking
in subset: ModelSubset

Output Layer
type: Class
subclass of: Layer
definition: A layer containing the last neurons in the network that produces given outputs for the program.
in subset: LayerSubset
comment: The output layer in an artificial neural network is the last layer of neurons that produces given outputs for the program. Though they are made much like other artificial neurons in the neural network, output layer neurons may be built or observed in a different way, given that they are the last “actor” nodes on the network.

Axiom
annotated source: Output Layer
annotated property: definition
annotated target: A layer containing the last neurons in the network that produces given outputs for the program.
has db xref: output-layer-neural-networks

PReLU Layer
type: Class
subclass of: Activation Layer
definition: An activation layer that applies parametric rectified linear unit function element-wise.
in subset: LayerSubset
comment: Parametric Rectified Linear Unit.

Axiom
annotated source: PReLU Layer
annotated property: definition
annotated target: An activation layer that applies parametric rectified linear unit function element-wise.
has db xref: PReLU

Perceptron
type: Class
subclass of: Artificial Neural Network
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: An artificial neural network with a supervised learning algorithm for binary classification using a linear predictor function.
has exact synonym: FFN
has exact synonym: Feed-Forward Network
has exact synonym: SLP
has exact synonym: Single Layer Perceptron
in subset: NetworkSubset
comment: Layers: Input, Output

Permute Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that permutes the dimensions of the input according to a given pattern.
in subset: LayerSubset
comment: Permutes the dimensions of the input according to a given pattern. Useful e.g. connecting RNNs and convnets.

Axiom
annotated source: Permute Layer
annotated property: definition
annotated target: A reshaping layer that permutes the dimensions of the input according to a given pattern.
has db xref: Permute

Personalized LLM
type: Class
subclass of: Large Language Model
definition: A large language model that adapts its language modeling and generation to the preferences style and persona of individual users or audiences.
has exact synonym: Personalized Large Language Model
has related synonym: user adaptation LLM
in subset: ModelSubset

Policy Layer
type: Class
subclass of: Layer
definition: A layer that, after taking a set of states or values as input, predicts a probability distribution of actions to take.
in subset: LayerSubset

Pooling Layer
type: Class
subclass of: Layer
definition: A layer that serves to mitigate the sensitivity of convolutional layers to location and spatially downsample representations.
in subset: LayerSubset
comment: Pooling layers serve the dual purposes of mitigating the sensitivity of convolutional layers to location and of spatially downsampling representations.

Axiom
annotated source: Pooling Layer
annotated property: definition
annotated target: A layer that serves to mitigate the sensitivity of convolutional layers to location and spatially downsample representations.
has db xref: pooling.html

Popularity Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias where more popular items are more exposed under-representing less popular items.
in subset: BiasSubset
comment: Selection bias where more popular items are more exposed, under-representing less popular items.

Axiom
annotated source: Popularity Bias
annotated property: definition
annotated target: A selection and sampling bias where more popular items are more exposed under-representing less popular items.
has db xref: NIST.SP.1270

Population Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias characterized by systematic distortions in demographics or other user characteristics between represented users and the target population.
in subset: BiasSubset

Axiom
annotated source: Population Bias
annotated property: definition
annotated target: A selection and sampling bias characterized by systematic distortions in demographics or other user characteristics between represented users and the target population.
has db xref: NIST.SP.1270

Preprocessing
type: Class
subclass of: Thing
definition: A process applied to raw data before it is used in a machine learning model, including tasks such as normalization, scaling, encoding, and transformation, to ensure the data is in an appropriate format and quality for analysis.
in subset: PreprocessingSubset

Axiom
annotated source: Preprocessing
annotated property: definition
annotated target: A process applied to raw data before it is used in a machine learning model, including tasks such as normalization, scaling, encoding, and transformation, to ensure the data is in an appropriate format and quality for analysis.
has db xref: ICDE.2019.00245

Preprocessing Layer
type: Class
subclass of: Layer
definition: A layer that performs data preprocessing operations.
in subset: LayerSubset

Axiom
annotated source: Preprocessing Layer
annotated property: definition
annotated target: A layer that performs data preprocessing operations.
has db xref: preprocessing layers

Presentation Bias
type: Class
subclass of: Individual Bias
definition: An individual bias arising from how information is presented on the Web via a user interface due to rating or ranking of output or through users' self-selected biased interaction.
in subset: BiasSubset

Axiom
annotated source: Presentation Bias
annotated property: definition
annotated target: An individual bias arising from how information is presented on the Web via a user interface due to rating or ranking of output or through users' self-selected biased interaction.
has db xref: NIST.SP.1270

Principal Component Analysis
type: Class
subclass of: Dimensionality Reduction
definition: A dimensionality reduction method for analyzing large datasets with high-dimensional features per observation increasing data interpretability while preserving maximum information and enabling visualization.
has exact synonym: PCA
in subset: MachineLearningSubset

Axiom
annotated source: Principal Component Analysis
annotated property: definition
annotated target: A dimensionality reduction method for analyzing large datasets with high-dimensional features per observation increasing data interpretability while preserving maximum information and enabling visualization.
has db xref: Principal component analysis

Probabilistic Graphical Model
type: Class
subclass of: Machine Learning Task
definition: A machine learning task in which a graph expresses the conditional dependence structure between random variables.
has exact synonym: Graphical Model
has exact synonym: PGM
has exact synonym: Structure Probabilistic Model
in subset: MachineLearningSubset

Axiom
annotated source: Probabilistic Graphical Model
annotated property: definition
annotated target: A machine learning task in which a graph expresses the conditional dependence structure between random variables.
has db xref: Graphical model

Probabilistic Hidden Layer
type: Class
subclass of: Hidden Layer
definition: A hidden layer that estimates the probability of a sample being within a certain category.
in subset: LayerSubset

Probabilistic Topic Model
type: Class
subclass of: Probabilistic Graphical Model
definition: A probabilistic graphical model that uses statistical techniques to analyze the words in each text to discover common themes their connections and their changes over time.
in subset: MachineLearningSubset

Axiom
annotated source: Probabilistic Topic Model
annotated property: definition
annotated target: A probabilistic graphical model that uses statistical techniques to analyze the words in each text to discover common themes their connections and their changes over time.
has db xref: prodlda.html

Processing Bias
type: Class
subclass of: Computational Bias
definition: A computational bias resulting from judgment modulated by affect influenced by the level of efficacy and efficiency in information processing.
has exact synonym: Validation Bias
in subset: BiasSubset
comment: Judgment modulated by affect, influenced by the level of efficacy and efficiency in information processing; often referred to as aesthetic judgment in cognitive sciences.

Axiom
annotated source: Processing Bias
annotated property: definition
annotated target: A computational bias resulting from judgment modulated by affect influenced by the level of efficacy and efficiency in information processing.
has db xref: Bias (statistics)

Prompt-based Fine-Tuning LLM
type: Class
subclass of: Large Language Model
definition: A large language model which is fine-tuned on a small number of examples or prompts, rather than full task datasets. This allows for rapid adaptation to new tasks with limited data, leveraging the model's few-shot learning capabilities.
has exact synonym: Prompt-based Fine-Tuning Large Language Model
has exact synonym: Prompt-tuned Large Language Model
has related synonym: few-shot learning
has related synonym: in-context learning
in subset: ModelSubset

Proportional Hazards Model
type: Class
subclass of: Survival Analysis
definition: A regression analysis for survival analysis where the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate.
in subset: MachineLearningSubset

Axiom
annotated source: Proportional Hazards Model
annotated property: definition
annotated target: A regression analysis for survival analysis where the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate.
has db xref: Proportional hazards model

RNN Layer
type: Class
subclass of: Recurrent Layer
definition: The base class for recurrent layers.
in subset: LayerSubset
comment: Base class for recurrent layers.

Axiom
annotated source: RNN Layer
annotated property: definition
annotated target: The base class for recurrent layers.
has db xref: RNN

Radial Basis Network
type: Class
subclass of: Deep Feed-Forward Network
definition: A deep feedforward network that uses radial basis functions as activation functions for pattern recognition and interpolation.
has exact synonym: RBFN
has exact synonym: RBN
has exact synonym: Radial Basis Function Network
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: Radial Basis Network
annotated property: definition
annotated target: A deep feedforward network that uses radial basis functions as activation functions for pattern recognition and interpolation.
has db xref: Radial basis function network

RandomBrightness Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly adjusts brightness during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly adjusts brightness during training. This layer will randomly increase/reduce the brightness for the input RGB images. At inference time, the output will be identical to the input. Call the layer with training=True to adjust the brightness of the input. Note that different brightness adjustment factors will be apply to each the images in the batch.

Axiom
annotated source: RandomBrightness Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly adjusts brightness during training.
has db xref: RandomBrightness

RandomContrast Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly adjusts contrast during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly adjusts contrast during training. This layer will randomly adjust the contrast of an image or images by a random factor. Contrast is adjusted independently for each channel of each image during training. For each channel, this layer computes the mean of the image pixels in the channel and then adjusts each component x of each pixel to (x - mean) * contrast_factor + mean. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and in integer or floating point dtype. By default, the layer will output floats. The output value will be clipped to the range [0, 255], the valid range of RGB colors.

Axiom
annotated source: RandomContrast Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly adjusts contrast during training.
has db xref: RandomContrast

RandomCrop Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly crops images during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly crops images during training. During training, this layer will randomly choose a location to crop images down to a target size. The layer will crop all the images in the same batch to the same cropping location. At inference time, and during training if an input image is smaller than the target size, the input will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. If you need to apply random cropping at inference time, set training to True when calling the layer. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.

Axiom
annotated source: RandomCrop Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly crops images during training.
has db xref: RandomCrop

Random Effects Model
type: Class
subclass of: Regression Analysis
definition: A regression analysis where the model parameters are random variables.
has exact synonym: REM
in subset: MachineLearningSubset

Axiom
annotated source: Random Effects Model
annotated property: definition
annotated target: A regression analysis where the model parameters are random variables.
has db xref: Random effects model

RandomFlip Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly flips images during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly flips images during training. This layer will flip the images horizontally and or vertically based on the mode attribute. During inference time, the output will be identical to input. Call the layer with training=True to flip the input. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.

Axiom
annotated source: RandomFlip Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly flips images during training.
has db xref: RandomFlip

Random Forest
type: Class
subclass of: Ensemble Learning
definition: An ensemble learning method for classification regression and other tasks that constructs a multitude of decision trees during training.
in subset: MachineLearningSubset

Axiom
annotated source: Random Forest
annotated property: definition
annotated target: An ensemble learning method for classification regression and other tasks that constructs a multitude of decision trees during training.
has db xref: Random forest

RandomHeight Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly varies image height during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly varies image height during training. This layer adjusts the height of a batch of images by a random factor. The input should be a 3D (unbatched) or 4D (batched) tensor in the "channels_last" image data format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. By default, this layer is inactive during inference.

Axiom
annotated source: RandomHeight Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly varies image height during training.
has db xref: RandomHeight

RandomRotation Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly rotates images during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly rotates images during training.

Axiom
annotated source: RandomRotation Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly rotates images during training.
has db xref: RandomRotation

RandomTranslation Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly translates images during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly translates images during training. This layer will apply random translations to each image during training, filling empty space according to fill_mode. aInput pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.

Axiom
annotated source: RandomTranslation Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly translates images during training.
has db xref: RandomTranslation

RandomWidth Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly varies image width during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly varies image width during training. This layer will randomly adjusts the width of a batch of images of a batch of images by a random factor. The input should be a 3D (unbatched) or 4D (batched) tensor in the "channels_last" image data format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. By default, this layer is inactive during inference.

Axiom
annotated source: RandomWidth Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly varies image width during training.
has db xref: RandomWidth

RandomZoom Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that randomly zooms in or out on images during training.
in subset: LayerSubset
comment: A preprocessing layer which randomly zooms images during training. This layer will randomly zoom in or out on each axis of an image independently, filling empty space according to fill_mode.Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.

Axiom
annotated source: RandomZoom Layer
annotated property: definition
annotated target: An image preprocessing layer that randomly zooms in or out on images during training.
has db xref: RandomZoom

Ranking Bias
type: Class
subclass of: Anchoring Bias
definition: An anchoring bias characterized by the idea that top-ranked results are the most relevant and important leading to more clicks than other results.
in subset: BiasSubset

Axiom
annotated source: Ranking Bias
annotated property: definition
annotated target: An anchoring bias characterized by the idea that top-ranked results are the most relevant and important leading to more clicks than other results.
has db xref: NIST.SP.1270

Rashomon Effect Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by differences in perspective memory recall interpretation and reporting of the same event by multiple persons or witnesses.
has exact synonym: Rashomon Effect
has exact synonym: Rashomon Principle
in subset: BiasSubset
comment: Differences in perspective, memory, recall, interpretation, and reporting of the same event by multiple persons or witnesses.

Axiom
annotated source: Rashomon Effect Bias
annotated property: definition
annotated target: An individual bias characterized by differences in perspective memory recall interpretation and reporting of the same event by multiple persons or witnesses.
has db xref: NIST.SP.1270

ReLU Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that returns max(x 0) the element-wise maximum of 0 and the input tensor.
has exact synonym: ReLU
has exact synonym: Rectified Linear Unit
in subset: ActivationFunctionSubset
in subset: FunctionSubset
comment: The ReLU activation function returns: max(x, 0), the element-wise maximum of 0 and the input tensor.

Axiom
annotated source: ReLU Function
annotated property: definition
annotated target: A mathematical function that returns max(x 0) the element-wise maximum of 0 and the input tensor.
has db xref: relu

ReLU Layer
type: Class
subclass of: Activation Layer
definition: An activation layer that applies the rectified linear unit function element-wise.
in subset: LayerSubset
comment: Rectified Linear Unit activation function. With default values, it returns element-wise max(x, 0).

Axiom
annotated source: ReLU Layer
annotated property: definition
annotated target: An activation layer that applies the rectified linear unit function element-wise.
has db xref: ReLU

Reasoning LLM
type: Class
subclass of: Large Language Model
definition: A large language model that incorporates explicit reasoning capabilities leveraging logical rules axioms or external knowledge to make deductive inferences during language tasks.
has exact synonym: Rational Large Language Model
has exact synonym: Reasoning Large Language Model
has related synonym: logical inferences
has related synonym: reasoning
in subset: ModelSubset

Axiom
annotated source: Reasoning LLM
annotated property: definition
annotated target: A large language model that incorporates explicit reasoning capabilities leveraging logical rules axioms or external knowledge to make deductive inferences during language tasks.
has db xref: 2023.acl-long.347

Recurrent Layer
type: Class
subclass of: Layer
definition: A layer composed of recurrent units with the number equal to the hidden size of the layer.
in subset: LayerSubset

Axiom
annotated source: Recurrent Layer
annotated property: definition
annotated target: A layer composed of recurrent units with the number equal to the hidden size of the layer.
has db xref: recurrent-layer

Recurrent Neural Network
type: Class
subclass of: Network
definition: A network with connections forming a directed graph along a temporal sequence enabling dynamic behavior.
has exact synonym: RN
has exact synonym: RecNN
has exact synonym: Recurrent Network
in subset: NetworkSubset

Recursive LLM
type: Class
subclass of: Language Model
definition: A language model that uses recursive neural network architectures like TreeLSTMs to learn syntactic composition functions improving systematic generalization abilities.
has exact synonym: Recursive Large Language Model
has exact synonym: Self-Attending Large Language Model
has related synonym: iterative refinement
has related synonym: self-attention
in subset: ModelSubset

Axiom
annotated source: Recursive LLM
annotated property: definition
annotated target: A language model that uses recursive neural network architectures like TreeLSTMs to learn syntactic composition functions improving systematic generalization abilities.
has db xref: aaai.v33i01.33017450

Recursive Language Model
type: Class
subclass of: Deep Neural Network
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Memory Cell Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: A deep neural network that uses recursive neural network architectures like TreeLSTMs to learn syntactic composition functions improving systematic generalization abilities.
has exact synonym: RLM
has related synonym: Compositional generalization
in subset: ModelSubset
comment: Layers: Input, Memory Cell, Output

Axiom
annotated source: Recursive Language Model
annotated property: definition
annotated target: A deep neural network that uses recursive neural network architectures like TreeLSTMs to learn syntactic composition functions improving systematic generalization abilities.
has db xref: Recurrent neural network

Recursive Neural Network
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that recursively applies weights over structured input to generate structured or scalar predictions.
has exact synonym: RecuNN
has exact synonym: RvNN
in subset: NetworkSubset

Axiom
annotated source: Recursive Neural Network
annotated property: definition
annotated target: A deep neural network that recursively applies weights over structured input to generate structured or scalar predictions.
has db xref: Recursive neural network

Regression Analysis
type: Class
subclass of: Supervised Learning
definition: A set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables.
has exact synonym: Regression analysis
has exact synonym: Regression model
in subset: MachineLearningSubset

Axiom
annotated source: Regression Analysis
annotated property: definition
annotated target: A set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables.
has db xref: Regression analysis

Regularization Layer
type: Class
subclass of: Layer
definition: A layer that applies penalties on layer parameters or layer activity during optimization summed into the loss function that the network optimizes.
in subset: LayerSubset
comment: Regularizers allow you to apply penalties on layer parameters or layer activity during optimization. These penalties are summed into the loss function that the network optimizes. Regularization penalties are applied on a per-layer basis.

Axiom
annotated source: Regularization Layer
annotated property: definition
annotated target: A layer that applies penalties on layer parameters or layer activity during optimization summed into the loss function that the network optimizes.
has db xref: regularizers

Reinforcement Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that do not require labeled input/output pairs or explicit correction of sub-optimal actions focusing instead on balancing exploration and exploitation to optimize performance over time.
in subset: MachineLearningSubset

Axiom
annotated source: Reinforcement Learning
annotated property: definition
annotated target: A machine learning task focused on methods that do not require labeled input/output pairs or explicit correction of sub-optimal actions focusing instead on balancing exploration and exploitation to optimize performance over time.
has db xref: Reinforcement learning

Reinforcement Learning LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is fine-tuned using reinforcement learning where the model receives rewards for generating text that satisfies certain desired properties or objectives improving the quality safety or alignment of generated text.
has exact synonym: RL-Large Language Model
has exact synonym: Reinforcement Learning Large Language Model
has related synonym: decision transformers
has related synonym: reward modeling
in subset: ModelSubset
comment: An RL-LLM is a language model fine-tuned using reinforcement learning, where the model receives rewards for generating text that satisfies certain desired properties or objectives. This can improve the quality, safety, or alignment of generated text.

RepeatVector Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that repeats the input n times.
in subset: LayerSubset
comment: Repeats the input n times.

Axiom
annotated source: RepeatVector Layer
annotated property: definition
annotated target: A reshaping layer that repeats the input n times.
has db xref: RepeatVector

Representation Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias due to non-random sampling of subgroups making trends non-generalizable to new populations.
in subset: BiasSubset
comment: Bias due to non-random sampling of subgroups, making trends non-generalizable to new populations.

Axiom
annotated source: Representation Bias
annotated property: definition
annotated target: A selection and sampling bias due to non-random sampling of subgroups making trends non-generalizable to new populations.
has db xref: NIST.SP.1270

Representation Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that discovers representations required for feature detection or classification from raw data.
has exact synonym: Feature Learning
in subset: NetworkSubset
comment: Discovering representations required for feature detection or classification from raw data.

Axiom
annotated source: Representation Learning
annotated property: definition
annotated target: A deep neural network that discovers representations required for feature detection or classification from raw data.
has db xref: Feature learning

Rescaling Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that rescales input values to a new range.
in subset: LayerSubset

Axiom
annotated source: Rescaling Layer
annotated property: definition
annotated target: An image preprocessing layer that rescales input values to a new range.
has db xref: Rescaling

Reshape Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that reshapes the inputs into the given shape.
in subset: LayerSubset

Axiom
annotated source: Reshape Layer
annotated property: definition
annotated target: A reshaping layer that reshapes the inputs into the given shape.
has db xref: Reshape

Reshaping Layer
type: Class
subclass of: Layer
definition: A layer that is used to change the shape of the input.
has exact synonym: Reshape Layer
in subset: LayerSubset
comment: Reshape layers are used to change the shape of the input.

Axiom
annotated source: Reshaping Layer
annotated property: definition
annotated target: A layer that is used to change the shape of the input.
has db xref: reshape

Residual Neural Network
type: Class
subclass of: Deep Neural Network
subclass of: Restriction
  on property: has part
  some values from: Addition Layer
subclass of: Restriction
  on property: has part
  some values from: BatchNormalization Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: ReLU Layer
subclass of: Restriction
  on property: has part
  some values from: Weighted Layer
definition: A deep neural network that employs skip connections to bypass layers facilitating learning of residual functions.
has exact synonym: DRN
has exact synonym: Deep Residual Network
has exact synonym: ResNN
has exact synonym: ResNet
in subset: NetworkSubset
comment: Layers: Input, Weight, BN, ReLU, Weight, BN, Addition, ReLU

Axiom
annotated source: Residual Neural Network
annotated property: definition
annotated target: A deep neural network that employs skip connections to bypass layers facilitating learning of residual functions.
has db xref: Residual neural network

Resizing Layer
type: Class
subclass of: Image Preprocessing Layer
definition: An image preprocessing layer that resizes images to a target size.
in subset: LayerSubset
comment: A preprocessing layer which resizes images. This layer resizes an image input to a target height and width. The input should be a 4D (batched) or 3D (unbatched) tensor in "channels_last" format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. This layer can be called on tf.RaggedTensor batches of input images of distinct sizes, and will resize the outputs to dense tensors of uniform size.

Axiom
annotated source: Resizing Layer
annotated property: definition
annotated target: An image preprocessing layer that resizes images to a target size.
has db xref: Resizing

Restricted Boltzmann Machine
type: Class
subclass of: Boltzmann Machine Network
definition: A Boltzmann machine network that learns the probability distribution of its input data.
has exact synonym: RBM
in subset: NetworkSubset
comment: Layers: Backfed Input, Probabilistic Hidden

Axiom
annotated source: Restricted Boltzmann Machine
annotated property: definition
annotated target: A Boltzmann machine network that learns the probability distribution of its input data.
has db xref: Restricted Boltzmann machine

Retrieval-Augmented LLM
type: Class
subclass of: Large Language Model
definition: A large language model which combines a pre-trained language model with a retrieval system that can access external knowledge sources. This allows the model to condition its generation on relevant retrieved knowledge, improving factual accuracy and knowledge grounding.
has exact synonym: Retrieval-Augmented Large Language Model
has related synonym: knowledge grounding
has related synonym: open-book question answering
in subset: ModelSubset

Ridge Regression
type: Class
subclass of: Regression Analysis
definition: A regression analysis that estimates the coefficients of multiple regression models in scenarios where the independent variables are highly correlated.
in subset: MachineLearningSubset

Axiom
annotated source: Ridge Regression
annotated property: definition
annotated target: A regression analysis that estimates the coefficients of multiple regression models in scenarios where the independent variables are highly correlated.
has db xref: Ridge regression

SELU Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that multiplies scale (> 1) with the output of the ELU function to ensure a slope larger than one for positive inputs.
has exact synonym: SELU
has exact synonym: Scaled Exponential Linear Unit
in subset: ActivationFunctionSubset
in subset: FunctionSubset

Axiom
annotated source: SELU Function
annotated property: definition
annotated target: A mathematical function that multiplies scale (> 1) with the output of the ELU function to ensure a slope larger than one for positive inputs.
has db xref: selu

Seasonal Autoregressive Integrated Moving-Average
type: Class
subclass of: Model
definition: A model that extends ARIMA, explicitly supporting univariate time series data with a seasonal component, combining seasonal differencing with ARIMA modeling.
has exact synonym: SARIMA
in subset: ModelSubset

Selection And Sampling Bias
type: Class
subclass of: Computational Bias
definition: A computational bias introduced by non-random selection of individuals groups or data failing to ensure representativeness.
has exact synonym: Sampling Bias
has exact synonym: Selection Bias
has exact synonym: Selection Effect
in subset: BiasSubset
comment: Bias introduced by non-random selection of individuals, groups, or data, failing to ensure representativeness.

Axiom
annotated source: Selection And Sampling Bias
annotated property: definition
annotated target: A computational bias introduced by non-random selection of individuals groups or data failing to ensure representativeness.
has db xref: NIST.SP.1270

Selective Adherence Bias
type: Class
subclass of: Individual Bias
definition: An individual bias characterized by the tendency to selectively adopt algorithmic advice that matches pre-existing beliefs and stereotypes.
in subset: BiasSubset

Axiom
annotated source: Selective Adherence Bias
annotated property: definition
annotated target: An individual bias characterized by the tendency to selectively adopt algorithmic advice that matches pre-existing beliefs and stereotypes.
has db xref: NIST.SP.1270

Self-Supervised LLM
type: Class
subclass of: Large Language Model
definition: A large language model which learns rich representations by solving pretext tasks that involve predicting parts of the input from other observed parts of the data, without relying on human-annotated labels.
has related synonym: Pretext tasks
in subset: ModelSubset

Self-supervised Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task that is intermediate between supervised and unsupervised learning and predicts parts of the input data from other observed parts without relying on human-annotated labels.
in subset: MachineLearningSubset

Axiom
annotated source: Self-supervised Learning
annotated property: definition
annotated target: A machine learning task that is intermediate between supervised and unsupervised learning and predicts parts of the input data from other observed parts without relying on human-annotated labels.
has db xref: Self-supervised learning

Semi-Supervised LLM
type: Class
subclass of: Large Language Model
definition: A large language model which combines self-supervised pretraining on unlabeled data with supervised fine-tuning on labeled task data.
has exact synonym: Semi-Supervised Large Language Model
has related synonym: self-training
in subset: ModelSubset

SeparableConvolution1D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that performs depthwise separable 1D convolution.
has exact synonym: SeparableConv1D Layer
in subset: LayerSubset
comment: Depthwise separable 1D convolution. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If use_bias is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.a

Axiom
annotated source: SeparableConvolution1D Layer
annotated property: definition
annotated target: A convolutional layer that performs depthwise separable 1D convolution.
has db xref: SeparableConv1D

SeparableConvolution2D Layer
type: Class
subclass of: Convolutional Layer
definition: A convolutional layer that performs depthwise separable 2D convolution.
has exact synonym: SeparableConv2D Layer
in subset: LayerSubset
comment: Depthwise separable 2D convolution. Separable convolutions consist of first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes the resulting output channels. The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step. Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.

Axiom
annotated source: SeparableConvolution2D Layer
annotated property: definition
annotated target: A convolutional layer that performs depthwise separable 2D convolution.
has db xref: SeparableConv2D

Sigmoid Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that applies the sigmoid activation function sigmoid(x) = 1 / (1 + exp(-x)) always returning a value between 0 and 1.
has exact synonym: tore
in subset: ActivationFunctionSubset
in subset: FunctionSubset
comment: Applies the sigmoid activation function sigmoid(x) = 1 / (1 + exp(-x)). For small values (<-5), sigmoid returns a value close to zero, and for large values (>5) the result of the function gets close to 1. Sigmoid is equivalent to a 2-element Softmax, where the second element is assumed to be zero. The sigmoid function always returns a value between 0 and 1.

Axiom
annotated source: Sigmoid Function
annotated property: definition
annotated target: A mathematical function that applies the sigmoid activation function sigmoid(x) = 1 / (1 + exp(-x)) always returning a value between 0 and 1.
has db xref: sigmoid

SimpleRNNCell Layer
type: Class
subclass of: Layer
definition: A layer that processes one step within the whole time sequence input for a SimpleRNN layer.
in subset: LayerSubset
comment: Cell class for SimpleRNN. This class processes one step within the whole time sequence input, whereas tf.keras.layer.SimpleRNN processes the whole sequence.

Axiom
annotated source: SimpleRNNCell Layer
annotated property: definition
annotated target: A layer that processes one step within the whole time sequence input for a SimpleRNN layer.
has db xref: SimpleRNNCell

SimpleRNN Layer
type: Class
subclass of: Recurrent Layer
definition: A recurrent layer that implements a fully-connected RNN where the output is to be fed back to input.
in subset: LayerSubset
comment: Fully-connected RNN where the output is to be fed back to input.

Axiom
annotated source: SimpleRNN Layer
annotated property: definition
annotated target: A recurrent layer that implements a fully-connected RNN where the output is to be fed back to input.
has db xref: SimpleRNN

Simpon's Paradox Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias where the association between two variables changes when controlling for another variable.
has exact synonym: Simpson's Paradox
in subset: BiasSubset

Axiom
annotated source: Simpon's Paradox Bias
annotated property: definition
annotated target: A selection and sampling bias where the association between two variables changes when controlling for another variable.
has db xref: NIST.SP.1270

Societal Bias
type: Class
subclass of: Systemic Bias
definition: A systemic bias characterized by being for or against groups or individuals based on social identities demographic factors or immutable physical characteristics often manifesting as stereotypes.
in subset: BiasSubset

Axiom
annotated source: Societal Bias
annotated property: definition
annotated target: A systemic bias characterized by being for or against groups or individuals based on social identities demographic factors or immutable physical characteristics often manifesting as stereotypes.
has db xref: NIST.SP.1270

Softmax Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function where the elements of the output vector are in range (0 1) and sum to 1 and each vector is handled independently.
in subset: ActivationFunctionSubset
in subset: FunctionSubset
comment: The elements of the output vector are in range (0, 1) and sum to 1. Each vector is handled independently. The axis argument sets which axis of the input the function is applied along. Softmax is often used as the activation for the last layer of a classification network because the result could be interpreted as a probability distribution. The softmax of each vector x is computed as exp(x) / tf.reduce_sum(exp(x)). The input values in are the log-odds of the resulting probability.

Axiom
annotated source: Softmax Function
annotated property: definition
annotated target: A mathematical function where the elements of the output vector are in range (0 1) and sum to 1 and each vector is handled independently.
has db xref: softmax

Softmax Layer
type: Class
subclass of: Activation Layer
definition: An activation layer that applies the softmax function to the inputs.
in subset: LayerSubset
comment: Softmax activation function.

Axiom
annotated source: Softmax Layer
annotated property: definition
annotated target: An activation layer that applies the softmax function to the inputs.
has db xref: Softmax

Softplus Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that is softplus(x) = log(exp(x) + 1).
in subset: ActivationFunctionSubset
in subset: FunctionSubset
comment: softplus(x) = log(exp(x) + 1)

Axiom
annotated source: Softplus Function
annotated property: definition
annotated target: A mathematical function that is softplus(x) = log(exp(x) + 1).
has db xref: softplus

Softsign Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that is softsign(x) = x / (abs(x) + 1).
in subset: ActivationFunctionSubset
in subset: FunctionSubset
comment: softsign(x) = x / (abs(x) + 1)

Axiom
annotated source: Softsign Function
annotated property: definition
annotated target: A mathematical function that is softsign(x) = x / (abs(x) + 1).
has db xref: softsign

Sparse Auto Encoder
type: Class
subclass of: Auto Encoder Network
definition: An autoencoder network with more hidden units than inputs that constrains only a few hidden units to be active at once.
has exact synonym: SAE
has exact synonym: Sparse AE
has exact synonym: Sparse Autoencoder
in subset: NetworkSubset
comment: Layers: Input, Hidden, Matched Output-Input

Sparse LLM
type: Class
subclass of: Large Language Model
definition: A large language model that uses techniques like pruning or quantization to reduce the number of non-zero parameters in the model making it more parameter-efficient and easier to deploy on resource-constrained devices.
has exact synonym: Sparse Large Language Model
has related synonym: model compression
has related synonym: parameter efficiency
in subset: ModelSubset

Sparse Learning
type: Class
subclass of: Representation Learning
definition: A representation learning network that finds sparse representations of input data as a linear combination of basic elements and identifies those elements.
has exact synonym: Sparse coding
has exact synonym: Sparse dictionary Learning
in subset: NetworkSubset

Axiom
annotated source: Sparse Learning
annotated property: definition
annotated target: A representation learning network that finds sparse representations of input data as a linear combination of basic elements and identifies those elements.
has db xref: Sparse dictionary learning

SpatialDropout1D Layer
type: Class
subclass of: Regularization Layer
definition: A regularization layer that performs the same function as Dropout but drops entire 1D feature maps instead of individual elements.
in subset: LayerSubset
comment: Spatial 1D version of Dropout. This version performs the same function as Dropout, however, it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout1D will help promote independence between feature maps and should be used instead.

Axiom
annotated source: SpatialDropout1D Layer
annotated property: definition
annotated target: A regularization layer that performs the same function as Dropout but drops entire 1D feature maps instead of individual elements.
has db xref: SpatialDropout1D

SpatialDropout2D Layer
type: Class
subclass of: Regularization Layer
definition: A regularization layer that performs the same function as Dropout but drops entire 2D feature maps instead of individual elements.
in subset: LayerSubset
comment: Spatial 2D version of Dropout. This version performs the same function as Dropout, however, it drops entire 2D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout2D will help promote independence between feature maps and should be used instead.a

Axiom
annotated source: SpatialDropout2D Layer
annotated property: definition
annotated target: A regularization layer that performs the same function as Dropout but drops entire 2D feature maps instead of individual elements.
has db xref: SpatialDropout2D

SpatialDropout3D Layer
type: Class
subclass of: Regularization Layer
definition: A regularization layer that performs the same function as Dropout but drops entire 3D feature maps instead of individual elements.
in subset: LayerSubset
comment: Spatial 3D version of Dropout. This version performs the same function as Dropout, however, it drops entire 3D feature maps instead of individual elements. If adjacent voxels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout3D will help promote independence between feature maps and should be used instead.

Axiom
annotated source: SpatialDropout3D Layer
annotated property: definition
annotated target: A regularization layer that performs the same function as Dropout but drops entire 3D feature maps instead of individual elements.
has db xref: SpatialDropout3D

Spatial Regression
type: Class
subclass of: Regression Analysis
definition: A regression analysis used to model spatial relationships.
in subset: MachineLearningSubset

Axiom
annotated source: Spatial Regression
annotated property: definition
annotated target: A regression analysis used to model spatial relationships.
has db xref: spatial-regression-models-arcgis

Spiking Hidden Layer
type: Class
subclass of: Hidden Layer
definition: A hidden layer that makes connections to an additional, heterogeneous hidden layer; modeled after biological neural networks.
in subset: LayerSubset

Axiom
annotated source: Spiking Hidden Layer
annotated property: definition
annotated target: A hidden layer that makes connections to an additional, heterogeneous hidden layer; modeled after biological neural networks.
has db xref: S0893-6080(97)00011-7

StackedRNNCells Layer
type: Class
subclass of: Layer
definition: A layer that allows a stack of RNN cells to behave as a single cell.
in subset: LayerSubset
comment: Wrapper allowing a stack of RNN cells to behave as a single cell. Used to implement efficient stacked RNNs.

Axiom
annotated source: StackedRNNCells Layer
annotated property: definition
annotated target: A layer that allows a stack of RNN cells to behave as a single cell.
has db xref: StackedRNNCells

Streetlight Effect Bias
type: Class
subclass of: Individual Bias
definition: An individual bias where people search only where it is easiest to look.
has exact synonym: Streetlight Effect
in subset: BiasSubset

Axiom
annotated source: Streetlight Effect Bias
annotated property: definition
annotated target: An individual bias where people search only where it is easiest to look.
has db xref: NIST.SP.1270

StringLookup Layer
type: Class
subclass of: Categorical Features Preprocessing Layer
definition: A categorical features preprocessing layer that maps string features to integer indices.
in subset: LayerSubset

Axiom
annotated source: StringLookup Layer
annotated property: definition
annotated target: A categorical features preprocessing layer that maps string features to integer indices.
has db xref: StringLookup

Subtract Layer
type: Class
subclass of: Merging Layer
definition: A merging layer that subtracts two inputs.
in subset: LayerSubset
comment: Layer that subtracts two inputs. It takes as input a list of tensors of size 2, both of the same shape, and returns a single tensor, (inputs[0] - inputs[1]), also of the same shape.

Axiom
annotated source: Subtract Layer
annotated property: definition
annotated target: A merging layer that subtracts two inputs.
has db xref: Subtract

Subword Segmentation
type: Class
subclass of: Data Preparation
definition: A data preparation that divides text into subword units which are smaller than words but larger than individual characters to improve the efficiency and effectiveness of natural language processing models by capturing meaningful subunits of words.
has exact synonym: Fragmentation
has exact synonym: Part-word Division
has related synonym: Byte Pair Encoding
has related synonym: SentencePiece
in subset: PreprocessingSubset

Sunk Cost Fallacy Bias
type: Class
subclass of: Individual Bias
definition: A bias characterized by the tendency to continue an endeavor due to previously invested resources despite costs outweighing benefits.
has exact synonym: Sunk Cost Fallacy
in subset: BiasSubset
comment: The tendency to continue an endeavor due to previously invested resources, despite costs outweighing benefits.

Axiom
annotated source: Sunk Cost Fallacy Bias
annotated property: definition
annotated target: A bias characterized by the tendency to continue an endeavor due to previously invested resources despite costs outweighing benefits.
has db xref: NIST.SP.1270

Supervised Biclustering
type: Class
subclass of: Biclustering
definition: A biclustering task focused on methods that simultaneously cluster the rows and columns of a labeled matrix considering data labels to enhance cluster coherence.
has exact synonym: Supervised Block Clustering
has exact synonym: Supervised Co-clustering
has exact synonym: Supervised Joint Clustering
has exact synonym: Supervised Two-mode Clustering
has exact synonym: Supervised Two-way Clustering
in subset: MachineLearningSubset

Axiom
annotated source: Supervised Biclustering
annotated property: definition
annotated target: A biclustering task focused on methods that simultaneously cluster the rows and columns of a labeled matrix considering data labels to enhance cluster coherence.
has db xref: Biclustering

Supervised Clustering
type: Class
subclass of: Clustering
definition: A clustering focused on methods that group labeled objects such that objects in the same group have similar labels relative to those in other groups.
has exact synonym: Supervised Cluster Analysis
in subset: MachineLearningSubset

Axiom
annotated source: Supervised Clustering
annotated property: definition
annotated target: A clustering focused on methods that group labeled objects such that objects in the same group have similar labels relative to those in other groups.
has db xref: Cluster analysis

Supervised Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that learn a function mapping input to output based on example input-output pairs.
in subset: MachineLearningSubset

Axiom
annotated source: Supervised Learning
annotated property: definition
annotated target: A machine learning task focused on methods that learn a function mapping input to output based on example input-output pairs.
has db xref: Supervised learning

Support Vector Machine
type: Class
subclass of: Network
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: A network with supervised learning models for classification and regression that maps training examples to points in space maximizing the gap between categories.
has exact synonym: SVM
has exact synonym: SVN
has exact synonym: Supper Vector Network
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: Support Vector Machine
annotated property: definition
annotated target: A network with supervised learning models for classification and regression that maps training examples to points in space maximizing the gap between categories.
has db xref: Support-vector machine

Survival Analysis
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods for analyzing the expected duration of time until one or more events occur such as death in biological organisms or failure in mechanical systems.
in subset: MachineLearningSubset

Axiom
annotated source: Survival Analysis
annotated property: definition
annotated target: A machine learning task focused on methods for analyzing the expected duration of time until one or more events occur such as death in biological organisms or failure in mechanical systems.
has db xref: Survival analysis

Survivorship Bias
type: Class
subclass of: Processing Bias
definition: A processing bias characterized by the tendency to focus on items observations or people that "survive" a selection process overlooking those that did not.
in subset: BiasSubset
comment: The tendency to focus on items, observations, or people that "survive" a selection process, overlooking those that did not.

Axiom
annotated source: Survivorship Bias
annotated property: definition
annotated target: A processing bias characterized by the tendency to focus on items observations or people that "survive" a selection process overlooking those that did not.
has db xref: NIST.SP.1270

Swish Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that is x*sigmoid(x) a smooth non-monotonic function that consistently matches or outperforms ReLU on deep networks.
in subset: ActivationFunctionSubset
in subset: FunctionSubset
comment: x*sigmoid(x). It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below.

Axiom
annotated source: Swish Function
annotated property: definition
annotated target: A mathematical function that is x*sigmoid(x) a smooth non-monotonic function that consistently matches or outperforms ReLU on deep networks.
has db xref: swish

Symmetrically Connected Network
type: Class
subclass of: Network
definition: A network that is a type of recurrent neural network where connections between units are symmetrical with equal weights in both directions.
has exact synonym: SCN
in subset: NetworkSubset
comment: Symmetrically connected networks are a type of recurrent neural network where connections between units are symmetrical, meaning they have equal weights in both directions. This structure allows the network to maintain consistent information flow and equilibrium.

Axiom
annotated source: Symmetrically Connected Network
annotated property: definition
annotated target: A network that is a type of recurrent neural network where connections between units are symmetrical with equal weights in both directions.
has db xref: https://ieeexplore.ieee.org/document/287176

SyncBatchNorm Layer
type: Class
subclass of: BatchNormalization Layer
definition: A batch normalization layer that applies synchronous Batch Normalization across multiple devices.
has exact synonym: SyncBatchNorm
in subset: LayerSubset
comment: Applies Batch Normalization over a N-Dimensional input (a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

Axiom
annotated source: SyncBatchNorm Layer
annotated property: definition
annotated target: A batch normalization layer that applies synchronous Batch Normalization across multiple devices.
has db xref: normalization-layers

Systemic Bias
type: Class
subclass of: Bias
definition: A bias resulting from procedures and practices of systems that operate in ways which result in certain social groups being advantaged or favored and others being disadvantaged or devalued.
in subset: BiasSubset

Axiom
annotated source: Systemic Bias
annotated property: definition
annotated target: A bias resulting from procedures and practices of systems that operate in ways which result in certain social groups being advantaged or favored and others being disadvantaged or devalued.
has db xref: NIST.SP.1270

Tanh Function
type: Class
subclass of: Mathematical Function
definition: A mathematical function that is the hyperbolic tangent activation function.
has exact synonym: hyperbolic tangent
in subset: ActivationFunctionSubset
in subset: FunctionSubset
comment: Hyperbolic tangent activation function.

Axiom
annotated source: Tanh Function
annotated property: definition
annotated target: A mathematical function that is the hyperbolic tangent activation function.
has db xref: tanh

Temporal Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias arising from differences in populations and behaviors over time.
in subset: BiasSubset

Axiom
annotated source: Temporal Bias
annotated property: definition
annotated target: A selection and sampling bias arising from differences in populations and behaviors over time.
has db xref: NIST.SP.1270

Text Preprocessing Layer
type: Class
subclass of: Layer
definition: A layer that performs text data preprocessing operations.
in subset: LayerSubset

Axiom
annotated source: Text Preprocessing Layer
annotated property: definition
annotated target: A layer that performs text data preprocessing operations.
has db xref: preprocessing layers

TextVectorization Layer
type: Class
subclass of: Text Preprocessing Layer
definition: A text preprocessing layer that maps text features to integer sequences.
in subset: LayerSubset

Axiom
annotated source: TextVectorization Layer
annotated property: definition
annotated target: A text preprocessing layer that maps text features to integer sequences.
has db xref: TextVectorization

Threshold Autoregressive
type: Class
subclass of: Model
definition: A model that allows for different autoregressive processes depending on the regime or state of the time series, enabling the capture of nonlinear behaviors.
has exact synonym: TAR
in subset: ModelSubset

Axiom
annotated source: Threshold Autoregressive
annotated property: definition
annotated target: A model that allows for different autoregressive processes depending on the regime or state of the time series, enabling the capture of nonlinear behaviors.
has db xref: https://dx.doi.org/10.1080/01621459.1989.10478760

ThresholdedReLU Layer
type: Class
subclass of: Activation Layer
definition: An activation layer that applies the thresholded rectified linear unit function element-wise.
in subset: LayerSubset
comment: Thresholded Rectified Linear Unit.

Axiom
annotated source: ThresholdedReLU Layer
annotated property: definition
annotated target: An activation layer that applies the thresholded rectified linear unit function element-wise.
has db xref: ThresholdedReLU

TimeDistributed Layer
type: Class
subclass of: Recurrent Layer
definition: A recurrent layer that applies a layer to every temporal slice of an input.
in subset: LayerSubset
comment: This wrapper allows to apply a layer to every temporal slice of an input. Every input should be at least 3D, and the dimension of index one of the first input will be considered to be the temporal dimension. Consider a batch of 32 video samples, where each sample is a 128x128 RGB image with channels_last data format, across 10 timesteps. The batch input shape is (32, 10, 128, 128, 3). You can then use TimeDistributed to apply the same Conv2D layer to each of the 10 timesteps, independently:

Axiom
annotated source: TimeDistributed Layer
annotated property: definition
annotated target: A recurrent layer that applies a layer to every temporal slice of an input.
has db xref: TimeDistributed

Time Series Analysis
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods for analyzing time series data to extract meaningful statistics and characteristics.
in subset: MachineLearningSubset
comment: Methods for analyzing time series data to extract meaningful statistics and characteristics.

Axiom
annotated source: Time Series Analysis
annotated property: definition
annotated target: A machine learning task focused on methods for analyzing time series data to extract meaningful statistics and characteristics.
has db xref: Time series

Time Series Forecasting
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that predict future values based on previously observed values.
in subset: MachineLearningSubset
comment: Methods that predict future values based on previously observed values.

Axiom
annotated source: Time Series Forecasting
annotated property: definition
annotated target: A machine learning task focused on methods that predict future values based on previously observed values.
has db xref: Time series

Tokenization
type: Class
subclass of: Data Preparation
definition: A data preparation that converts a sequence of text into smaller meaningful units called tokens typically words or subwords for the purpose of analysis or processing by language models.
has exact synonym: Lexical Analysis
has exact synonym: Text Segmentation
in subset: PreprocessingSubset

TrainingStrategy
type: Class
subclass of: Thing
definition: A preprocessing used to train machine learning models including techniques such as supervised learning unsupervised learning reinforcement learning and transfer learning aimed at optimizing model performance.
has exact synonym: Instructional Methods
has exact synonym: Learning Techniques
in subset: TrainingStrategySubset

Transfer Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on methods that reuse or transfer information from previously learned tasks to facilitate the learning of new tasks.
in subset: NetworkSubset

Axiom
annotated source: Transfer Learning
annotated property: definition
annotated target: A machine learning task focused on methods that reuse or transfer information from previously learned tasks to facilitate the learning of new tasks.
has db xref: Transfer learning

Transfer Learning LLM
type: Class
subclass of: Large Language Model
definition: A large language model that leverages knowledge acquired during training on one task to improve performance on different but related tasks facilitating more efficient learning and adaptation.
has exact synonym: Transfer LLM
has related synonym: transfer learning
in subset: ModelSubset

Transformer LLM
type: Class
subclass of: Transformer Language Model
definition: A transformer language model with large training corpuses and sets of parameters that uses the transformer architecture based on multi-head attention mechanisms allowing it to contextualize tokens within a context window for effective language understanding and generation.
has exact synonym: Transformer Large Language Model
in subset: ModelSubset

Axiom
annotated source: Transformer LLM
annotated property: definition
annotated target: A transformer language model with large training corpuses and sets of parameters that uses the transformer architecture based on multi-head attention mechanisms allowing it to contextualize tokens within a context window for effective language understanding and generation.
has db xref: Transformer (deep learning architecture)

Transformer Language Model
type: Class
subclass of: Language Model
definition: A language model that uses the transformer architecture based on multi-head attention mechanisms allowing it to contextualize tokens within a context window for effective language understanding and generation.
has exact synonym: Transformer LM
in subset: ModelSubset

Axiom
annotated source: Transformer Language Model
annotated property: definition
annotated target: A language model that uses the transformer architecture based on multi-head attention mechanisms allowing it to contextualize tokens within a context window for effective language understanding and generation.
has db xref: https://arxiv.org/abs/1706.03762

Transformer Network
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that utilizes attention mechanisms to weigh the significance of input data.
in subset: NetworkSubset
comment: A transformer network utilizes attention mechanisms to weigh the significance of each part of the input data, widely used in natural language processing (NLP) and computer vision (CV).

Axiom
annotated source: Transformer Network
annotated property: definition
annotated target: A deep neural network that utilizes attention mechanisms to weigh the significance of input data.
has db xref: Transformer (machine Learning model)

Uncertainty Bias
type: Class
subclass of: Selection And Sampling Bias
definition: A selection and sampling bias favoring groups better represented in training data due to less prediction uncertainty.
in subset: BiasSubset
comment: Bias favoring groups better represented in training data, due to less prediction uncertainty.

Axiom
annotated source: Uncertainty Bias
annotated property: definition
annotated target: A selection and sampling bias favoring groups better represented in training data due to less prediction uncertainty.
has db xref: NIST.SP.1270

UnitNormalization Layer
type: Class
subclass of: Recurrent Layer
definition: A recurrent layer that normalizes a batch of inputs so that each input in the batch has a L2 norm equal to 1.
in subset: LayerSubset
comment: Unit normalization layer. Normalize a batch of inputs so that each input in the batch has a L2 norm equal to 1 (across the axes specified in axis).

Axiom
annotated source: UnitNormalization Layer
annotated property: definition
annotated target: A recurrent layer that normalizes a batch of inputs so that each input in the batch has a L2 norm equal to 1.
has db xref: UnitNormalization

Unsupervised Biclustering
type: Class
subclass of: Biclustering
definition: A biclustering task focused on methods that simultaneously cluster the rows and columns of an unlabeled input matrix to identify submatrices with coherent patterns.
has exact synonym: Unsupervised Block Clustering
has exact synonym: Unsupervised Co-clustering
has exact synonym: Unsupervised Joint Clustering
has exact synonym: Unsupervised Two-mode Clustering
has exact synonym: Unsupervised Two-way Clustering
in subset: MachineLearningSubset

Axiom
annotated source: Unsupervised Biclustering
annotated property: definition
annotated target: A biclustering task focused on methods that simultaneously cluster the rows and columns of an unlabeled input matrix to identify submatrices with coherent patterns.
has db xref: Biclustering

Unsupervised Clustering
type: Class
subclass of: Clustering
definition: A clustering focused on methods that group a set of unlabeled objects such that objects in the same group are more similar to each other than to those in other groups.
has exact synonym: Unsupervised Cluster Analysis
in subset: MachineLearningSubset

Axiom
annotated source: Unsupervised Clustering
annotated property: definition
annotated target: A clustering focused on methods that group a set of unlabeled objects such that objects in the same group are more similar to each other than to those in other groups.
has db xref: Cluster analysis

Unsupervised LLM
type: Class
subclass of: Large Language Model
definition: A large language model that is trained solely on unlabeled data using self-supervised objectives like masked language modeling without any supervised fine-tuning.
has exact synonym: Unsupervised Large Language Model
has related synonym: self-supervised
in subset: ModelSubset

Unsupervised Learning
type: Class
subclass of: Machine Learning Task
definition: A machine learning task focused on algorithms that learn patterns from unlabeled data.
in subset: MachineLearningSubset
comment: Algorithms that learn patterns from unlabeled data.

Axiom
annotated source: Unsupervised Learning
annotated property: definition
annotated target: A machine learning task focused on algorithms that learn patterns from unlabeled data.
has db xref: Unsupervised learning

Unsupervised Pretrained Network
type: Class
subclass of: Network
definition: A network that initializes a discriminative neural net from one trained using an unsupervised criterion.
has exact synonym: UPN
in subset: NetworkSubset
comment: Unsupervised pre-training initializes a discriminative neural net from one trained using an unsupervised criterion, aiding in optimization and overfitting issues.

Axiom
annotated source: Unsupervised Pretrained Network
annotated property: definition
annotated target: A network that initializes a discriminative neural net from one trained using an unsupervised criterion.
has db xref: unsupervised pre training

UpSampling1D Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that upsamples the input by repeating each temporal step size times along the time axis.
in subset: LayerSubset
comment: Upsampling layer for 1D inputs. Repeats each temporal step size times along the time axis.

Axiom
annotated source: UpSampling1D Layer
annotated property: definition
annotated target: A reshaping layer that upsamples the input by repeating each temporal step size times along the time axis.
has db xref: UpSampling1D

UpSampling2D Layer
type: Class
subclass of: Layer
definition: A layer that upsamples the input by repeating each row and column size times.
in subset: LayerSubset
comment: Upsampling layer for 2D inputs. Repeats the rows and columns of the data by size[0] and size[1] respectively.

Axiom
annotated source: UpSampling2D Layer
annotated property: definition
annotated target: A layer that upsamples the input by repeating each row and column size times.
has db xref: UpSampling2D

UpSampling3D Layer
type: Class
subclass of: Layer
definition: A layer that upsamples the input by repeating each depth
in subset: LayerSubset
comment: Upsampling layer for 3D inputs.

Axiom
annotated source: UpSampling3D Layer
annotated property: definition
annotated target: A layer that upsamples the input by repeating each depth
has db xref: UpSampling3D

Use And Interpretation Bias
type: Class
subclass of: Computational Bias
definition: A computational bias characterized by inappropriately analyzing ambiguous stimuli scenarios and events.
has exact synonym: Interpretive Bias
in subset: BiasSubset
comment: Bias inappropriately analyzing ambiguous stimuli, scenarios, and events.

Axiom
annotated source: Use And Interpretation Bias
annotated property: definition
annotated target: A computational bias characterized by inappropriately analyzing ambiguous stimuli scenarios and events.
has db xref: NIST.SP.1270

User Interaction Bias
type: Class
subclass of: Individual Bias
definition: An individual bias arising when a user imposes their own biases during interaction with data output results etc.
in subset: BiasSubset
comment: Bias arising when a user imposes their own biases during interaction with data, output, results, etc.

Axiom
annotated source: User Interaction Bias
annotated property: definition
annotated target: An individual bias arising when a user imposes their own biases during interaction with data output results etc.
has db xref: NIST.SP.1270

Variational Auto Encoder
type: Class
subclass of: Auto Encoder Network
subclass of: Restriction
  on property: has part
  some values from: Probabilistic Hidden Layer
definition: An autoencoder network that imposes a probabilistic structure on the latent space for unsupervised learning.
has exact synonym: VAE
in subset: NetworkSubset
comment: Layers: Input, Probabilistic Hidden, Matched Output-Input

Vector Autoregression
type: Class
subclass of: Model
definition: A model that captures the linear interdependencies among multiple time series, where each variable is modeled as a linear function of its own past values and the past values of all other variables in the system.
has exact synonym: VAR
in subset: ModelSubset

Vocabulary Reduction
type: Class
subclass of: Data Preparation
definition: A data preparation that limits the number of unique tokens in a language model's vocabulary by merging or eliminating less frequent tokens thereby optimizing computational efficiency and resource usage.
has exact synonym: Lexical Simplification
has exact synonym: Lexicon Pruning
has exact synonym: Vocabulary Condensation
in subset: PreprocessingSubset

Weighted Layer
type: Class
subclass of: Layer
definition: A layer of values to be applied to other cells or neurons in a network.
in subset: LayerSubset

Wrapper Layer
type: Class
subclass of: Layer
definition: A layer that augment the functionality of another layer.
in subset: LayerSubset
comment: Abstract wrapper base class. Wrappers take another layer and augment it in various ways. Do not use this class as a layer, it is only an abstract base class. Two usable wrappers are the TimeDistributed and Bidirectional wrappers.

Axiom
annotated source: Wrapper Layer
annotated property: definition
annotated target: A layer that augment the functionality of another layer.
has db xref: Wrapper

ZeroPadding1D Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that zero-pads the input along the time axis.
in subset: LayerSubset
comment: Zero-padding layer for 1D input (e.g. temporal sequence).

Axiom
annotated source: ZeroPadding1D Layer
annotated property: definition
annotated target: A reshaping layer that zero-pads the input along the time axis.
has db xref: ZeroPadding1D

ZeroPadding2D Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that zero-pads the input along the height and width dimensions.
in subset: LayerSubset
comment: Zero-padding layer for 2D input (e.g. picture). This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.

Axiom
annotated source: ZeroPadding2D Layer
annotated property: definition
annotated target: A reshaping layer that zero-pads the input along the height and width dimensions.
has db xref: ZeroPadding2D

ZeroPadding3D Layer
type: Class
subclass of: Reshaping Layer
definition: A reshaping layer that zero-pads the input along the depth
in subset: LayerSubset
comment: Zero-padding layer for 3D data (spatial or spatio-temporal).

Axiom
annotated source: ZeroPadding3D Layer
annotated property: definition
annotated target: A reshaping layer that zero-pads the input along the depth
has db xref: ZeroPadding3D

Zero-Shot Learning LLM
type: Class
subclass of: Large Language Model
definition: A large language model which performs tasks or understands concepts it has not explicitly been trained on, demonstrating a high degree of generalization and understanding.
has exact synonym: Zero-Shot LLM
has related synonym: zero-shot learning
in subset: ModelSubset

Zero-shot Learning
type: Class
subclass of: Deep Neural Network
definition: A deep neural network that predicts classes at test time from classes not observed during training.
has exact synonym: ZSL
in subset: NetworkSubset

Axiom
annotated source: Zero-shot Learning
annotated property: definition
annotated target: A deep neural network that predicts classes at test time from classes not observed during training.
has db xref: Zero-shot learning

node2vec
type: Class
subclass of: Machine Learning Task
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: A machine learning task designed to learn continuous feature representations for nodes in a graph by optimizing a neighborhood-preserving objective.
has exact synonym: N2V
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: node2vec
annotated property: definition
annotated target: A machine learning task designed to learn continuous feature representations for nodes in a graph by optimizing a neighborhood-preserving objective.
has db xref: Node2vec

node2vec-CBOW
type: Class
subclass of: node2vec
definition: A node2vec that predicts the current node from a window of surrounding context nodes, with the order of context nodes not influencing prediction.
has exact synonym: N2V-CBOW
has related synonym: CBOW
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: node2vec-CBOW
annotated property: definition
annotated target: A node2vec that predicts the current node from a window of surrounding context nodes, with the order of context nodes not influencing prediction.
has db xref: Node2vec

node2vec-SkipGram
type: Class
subclass of: node2vec
definition: A node2vec that uses the current node to predict the surrounding window of context nodes, weighing nearby context nodes more heavily than distant ones.
has exact synonym: N2V-SkipGram
has related synonym: SkipGram
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: node2vec-SkipGram
annotated property: definition
annotated target: A node2vec that uses the current node to predict the surrounding window of context nodes, weighing nearby context nodes more heavily than distant ones.
has db xref: Node2vec

t-Distributed Stochastic Neighbor embedding
type: Class
subclass of: Dimensionality Reduction
definition: A dimensionality reduction for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.
has exact synonym: t-SNE
has exact synonym: tSNE
in subset: MachineLearningSubset

Axiom
annotated source: t-Distributed Stochastic Neighbor embedding
annotated property: definition
annotated target: A dimensionality reduction for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.
has db xref: T-distributed stochastic neighbor embedding

word2vec
type: Class
subclass of: Machine Learning Task
subclass of: Restriction
  on property: has part
  some values from: Hidden Layer
subclass of: Restriction
  on property: has part
  some values from: Input Layer
subclass of: Restriction
  on property: has part
  some values from: Output Layer
definition: A machine learning task that generates distributed representations of words by training a shallow neural network model, which aims to predict the context of each word within a corpus. This algorithm captures semantic meanings of words through their contextual usage in the text.
has exact synonym: W2V
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: word2vec
annotated property: definition
annotated target: A machine learning task that generates distributed representations of words by training a shallow neural network model, which aims to predict the context of each word within a corpus. This algorithm captures semantic meanings of words through their contextual usage in the text.
has db xref: Word2vec

word2vec-CBOW
type: Class
subclass of: word2vec
definition: A word2vec that predicts the current word from a window of surrounding context words, ignoring the order of context words.
has exact synonym: W2V-CBOW
has related synonym: CBOW
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: word2vec-CBOW
annotated property: definition
annotated target: A word2vec that predicts the current word from a window of surrounding context words, ignoring the order of context words.
has db xref: Word2vec

word2vec-SkipGram
type: Class
subclass of: word2vec
definition: A word2vec that predicts surrounding context words from the current word, giving more weight to nearby context words than distant ones.
has exact synonym: W2V-SkipGram
has related synonym: SkipGram
in subset: NetworkSubset
comment: Layers: Input, Hidden, Output

Axiom
annotated source: word2vec-SkipGram
annotated property: definition
annotated target: A word2vec that predicts surrounding context words from the current word, giving more weight to nearby context words than distant ones.
has db xref: Word2vec