summaries of papers on deep learning.



*********************


* [[Week-1](https://github.com/gopala-kr/summary/tree/master/summaries/Week-1)] 15-Oct-2017 - DL history and basics : DBN, AlexNet, VGGNet, GoogLeNet, ResNet, RNN(speech evolution) 
* [[Week-2](https://github.com/gopala-kr/summary/tree/master/summaries/Week-2)] 22-Oct-2017 - DL methods : models, optimization, deep generative model, rnn/seq-to-seq model
* [[Week-3](https://github.com/gopala-kr/summary/tree/master/summaries/Week-3)] 29-Oct-2017 - DL methods : Neural Turing Machine,Reinforcement-Transfer-One-Shot Deep Learning
* [[Week-4](https://github.com/gopala-kr/summary/tree/master/summaries/Week-4)] 06-Nov-2017 - DL applications : nlp, object detection, visual tracking, image caption
* [[Week-5](https://github.com/gopala-kr/summary/tree/master/summaries/Week-5)] 12-Nov-2017 - DL applications : Machine Translation, Robotics, Art, Object Segmentation
* [[Week-6](https://github.com/gopala-kr/summary/tree/master/summaries/Week-6)] 19-Nov-2017 - recommender systems
* [[Week-7](https://github.com/gopala-kr/summary/tree/master/summaries/Week-7)] 26-Nov-2017 - ftm : 01/2011 - 12/2014
* [[Week-8](https://github.com/gopala-kr/summary/tree/master/summaries/Week-8)] 03-Dec-2017 - ftm : 01/2015 - 12/2015
* [[Week-9](https://github.com/gopala-kr/summary/tree/master/summaries/Week-9)] 10-Dec-2017 - ftm : 01/2016 - 05/2016
* [[Week-10](https://github.com/gopala-kr/summary/blob/master/summaries/Week-10)] 17-Dec-2017 - ftm : 06/2016 - 12/2016
* [[Week-11](https://github.com/gopala-kr/summary/tree/master/summaries/Week-11)] 24-Dec-2017 - ftm : 01/2017 - 07/2017
* [[Week-12](https://github.com/gopala-kr/summary/tree/master/summaries/Week-12)] 31-Dec-2017 - ftm : 08/2017 - 12/2017

----------------------------------


- [[Jan-2018](https://github.com/gopala-kr/summary/tree/master/summaries/Jan-2018)]
- [[Feb-2018](https://github.com/gopala-kr/summary/tree/master/summaries/Feb-2018)]
- [[March-2018](https://github.com/gopala-kr/summary/tree/master/summaries/March-2018)]
- [[April-2018](https://github.com/gopala-kr/summary/blob/master/summaries/April-2018/README.md)]
- [[May-2018](https://github.com/gopala-kr/summary/blob/master/summaries/May-2018/README.md)]
- [[June-2018](https://github.com/gopala-kr/summary/blob/master/summaries/June-2018/README.md)]


Articles

- [[Jan2March 2018](https://github.com/gopala-kr/summary/tree/master/articles#jan2march-2018)]  
- [[April2June 2018](https://github.com/gopala-kr/summary/tree/master/articles#april2june-2018)]
- [[2018 so far](https://github.com/gopala-kr/summary/blob/master/summaries/2018%20so%20far/README.md)]
- [[2018-Jan-Sep](https://github.com/gopala-kr/summary/blob/master/summaries/2018-Jan-Sep.md)]
- [[conferences-2018](https://github.com/gopala-kr/summary/blob/master/conferences-2018.md)]



-----------------------


![D8C8rEWWwAAYMVU](https://pbs.twimg.com/media/D8C8rEWWwAAYMVU.jpg:large)


------------

07/2019

- [How Did Humans Become So Creative? A Computational Approach](https://arxiv.org/abs/1308.5032v4) :star:
- [Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering](https://arxiv.org/abs/1907.04298v1)
- [Translating neural signals to text using a Brain-Machine Interface](https://arxiv.org/abs/1907.04265v1)
- [SVGD: A Virtual Gradients Descent Method for Stochastic Optimization](https://arxiv.org/pdf/1907.04021v1.pdf)
- [A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends](https://arxiv.org/pdf/1905.13294v3.pdf)
- [Large Memory Layers with Product Keys](https://arxiv.org/pdf/1907.05242v1.pdf) :star:
- [Large Scale Adversarial Representation Learning](https://arxiv.org/pdf/1907.02544v1.pdf)
- [Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning](https://arxiv.org/pdf/1907.04525v1.pdf)
- [Out-of-Distribution Detection Using Neural Rendering Generative Models](https://arxiv.org/pdf/1907.04572v1.pdf)
- [City-GAN: Learning architectural styles using a custom Conditional GAN architecture](https://arxiv.org/pdf/1907.05280v1.pdf)
- [Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds](https://arxiv.org/pdf/1907.05279v1.pdf)
- [Listen, Attend, Spell and Adapt: Speaker Adapted Sequence-to-Sequence ASR](https://arxiv.org/pdf/1907.04916v1.pdf)
- [A Review of Machine Learning Applications in Fuzzing](https://arxiv.org/pdf/1906.11133v1.pdf)
- [GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering](https://arxiv.org/pdf/1902.09506v3.pdf)
- [Machine Learning Kernel Method from a Quantum Generative Model](https://arxiv.org/pdf/1907.05103v1.pdf)
- [A Neural Architecture for Designing Truthful and Efficient Auctions](https://arxiv.org/pdf/1907.05181v1.pdf)
- [Fast geodesic shooting for landmark matching using CUDA](https://arxiv.org/ftp/arxiv/papers/1907/1907.04839.pdf)
- [The Role of Cooperation in Responsible AI Development](https://arxiv.org/pdf/1907.04534v1.pdf)
- [PreCall: A Visual Interface for Threshold Optimization in ML Model Selection](https://arxiv.org/abs/1907.05131v1)
- [Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges](https://arxiv.org/abs/1907.05019v1)
- [Playing Go without Game Tree Search Using Convolutional Neural Networks](https://arxiv.org/abs/1907.04658v1)
- [Incrementalizing RASA's Open-Source Natural Language Understanding Pipeline](https://arxiv.org/abs/1907.05403v1)
- [BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs](https://arxiv.org/abs/1907.05047v1)
- [Making AI Forget You: Data Deletion in Machine Learning](https://arxiv.org/pdf/1907.05012v1.pdf)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://arxiv.org/abs/1905.08883v2)
- [What does it mean to understand a neural network?](https://arxiv.org/abs/1907.06374v1)
- [Efficient Video Generation on Complex Datasets](https://arxiv.org/abs/1907.06571v1)
- [NPA: Neural News Recommendation with Personalized Attention](https://arxiv.org/pdf/1907.05559v1.pdf)
- [Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure](https://arxiv.org/abs/1907.05505v1)
- [Gated-SCNN: Gated Shape CNNs for Semantic Segmentation](https://arxiv.org/abs/1907.05740v1)
- [Task Selection Policies for Multitask Learning](https://arxiv.org/abs/1907.06214v1)
- [Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation](https://arxiv.org/abs/1907.06170v1)
- [A Neural Turing~Machine for Conditional Transition Graph Modeling](https://arxiv.org/abs/1907.06432v1)
- [A Differentiable Programming System to Bridge Machine Learning and Scientific Computing](https://arxiv.org/abs/1907.07587v2)
- [OCC: A Smart Reply System for Efficient In-App Communications](https://arxiv.org/abs/1907.08167v1)
- [A Survey of Data Quality Measurement and Monitoring Tools](https://arxiv.org/abs/1907.08138v1)
- [User-Interactive Machine Learning Model for Identifying Structural Relationships of Code Features](https://arxiv.org/abs/1907.07679v1)
- [ELG: An Event Logic Graph](https://arxiv.org/abs/1907.08015v1)
- [CraftAssist: A Framework for Dialogue-enabled Interactive Agents](https://arxiv.org/abs/1907.08584v1)
- [HAKE: Human Activity Knowledge Engine](https://arxiv.org/abs/1904.06539v3)
- [Probing Neural Network Comprehension of Natural Language Arguments](https://arxiv.org/abs/1907.07355v1)
- [Beyond Imitation: Generative and Variational Choreography via Machine Learning](https://arxiv.org/abs/1907.05297v1)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://arxiv.org/abs/1906.03327v1) :star:
- [Deep-SLAM++: Object-level RGBD SLAM based on class-specific deep shape priors](https://arxiv.org/abs/1907.09691v1)
- [Compositional Deep Learning](https://arxiv.org/abs/1907.08292v1)
- [SentiMATE: Learning to play Chess through Natural Language Processing](https://arxiv.org/abs/1907.08321v2)
- [Convolutional Reservoir Computing for World Models](https://arxiv.org/abs/1907.08040v1)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://arxiv.org/abs/1907.06902v1)
- [SlugBot: Developing a Computational Model andFramework of a Novel Dialogue Genre](https://arxiv.org/abs/1907.10658v1)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://arxiv.org/abs/1907.10902v1) :star:
- [Speech2Face: Learning the Face Behind a Voice](https://arxiv.org/abs/1905.09773v1)
- [Semi-parametric Object Synthesis](https://arxiv.org/abs/1907.10634v1)
- [INS: An Interactive Chinese News Synthesis System](https://arxiv.org/pdf/1907.10781v1.pdf)
- [Green AI](https://arxiv.org/pdf/1907.10597v2.pdf) :star:
- [AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence](https://arxiv.org/pdf/1905.10985v1.pdf)
- [U4D: Unsupervised 4D Dynamic Scene Understanding](https://arxiv.org/pdf/1907.09905v1.pdf)
- [How to Manipulate CNNs to Make Them Lie: the GradCAM Case](https://arxiv.org/abs/1907.10901v1)
- [SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications](https://arxiv.org/abs/1907.11093v1)
- [Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo](https://arxiv.org/abs/1710.10967v3)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692v1)
- [Benchmarking TPU, GPU, and CPU Platforms for Deep Learning](https://arxiv.org/pdf/1907.10701v1.pdf)
- [Towards AutoML in the presence of Drift: first results](https://arxiv.org/pdf/1907.10772v1.pdf)
- [RNNbow: Visualizing Learning via Backpropagation Gradients in Recurrent Neural Networks](https://arxiv.org/pdf/1907.12545v1.pdf)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://arxiv.org/pdf/1907.12412v1.pdf)
- [Deep Reinforcement Learning for Personalized Search Story Recommendation](https://arxiv.org/abs/1907.11754v1)
- [FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation](https://arxiv.org/abs/1907.12347v1)
- [Towards meta-learning for multi-target regression problems](https://arxiv.org/abs/1907.11277v1)
- [A Benchmark on Tricks for Large-scale Image Retrieval](https://arxiv.org/pdf/1907.11854v1.pdf)

-------------

06/2019

- [Tackling Climate Change with Machine Learning](https://arxiv.org/pdf/1906.05433.pdf) :star:
- [Mesh R-CNN](https://arxiv.org/abs/1906.02739v1)
- [Does Object Recognition Work for Everyone?](https://arxiv.org/abs/1906.02659v1)
- [Energy and Policy Considerations for Deep Learning in NLP](https://arxiv.org/abs/1906.02243v1)
- [An Introduction to Variational Autoencoders](https://arxiv.org/abs/1906.02691v1)
- [Text-based Editing of Talking-head Video](https://arxiv.org/abs/1906.01524v1)
- [A Multi-Pass GAN for Fluid Flow Super-Resolution](https://arxiv.org/abs/1906.01689v1)
- [IoT Network Security from the Perspective of Adversarial Deep Learning](https://arxiv.org/abs/1906.00076v1)
- [Deep learning for image segmentation-a short survey](https://arxiv.org/abs/1904.08483v2)
- [On the Realization of Compositionality in Neural Networks](https://arxiv.org/abs/1906.01634v2)
- [Butterfly Transform: An Efficient FFT Based Neural Architecture Design](https://arxiv.org/abs/1906.02256v1)
- [Automated Machine Learning: State-of-The-Art and Open Challenges](https://arxiv.org/abs/1906.02287v1)
- [Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise](https://arxiv.org/abs/1906.02355v1)
- [Automated Machine Learning with Monte-Carlo Tree Search (Extended Version)](https://arxiv.org/abs/1906.00170v1)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://arxiv.org/abs/1906.02361v1)
- [Automatic Reparameterisation of Probabilistic Programs](https://arxiv.org/abs/1906.03028v1)
- [Modeling e-Learners' Cognitive and Metacognitive Strategy in Comparative Question Solving](https://arxiv.org/abs/1906.03074v1)
- [Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach](https://arxiv.org/abs/1906.03098v1)
- [KERMIT: Generative Insertion-Based Modeling for Sequences](https://arxiv.org/pdf/1906.01604v1.pdf) :star:
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://arxiv.org/abs/1811.12359v3)
- [Monotonic Infinite Lookback Attention for Simultaneous Machine Translation](https://arxiv.org/abs/1906.05218v1)
- [Is Deep Learning an RG Flow?](https://arxiv.org/abs/1906.05212v1)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://arxiv.org/abs/1906.04341v1)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://arxiv.org/abs/1806.08342v1)
- [What Kind of Language Is Hard to Language-Model?](https://arxiv.org/abs/1906.04726v1)
- [Open Sesame: Getting Inside BERT's Linguistic Knowledge](https://arxiv.org/abs/1906.01698v1)
- [NAS-FCOS: Fast Neural Architecture Search for Object Detection](https://arxiv.org/abs/1906.04423v2)
- [DCEF: Deep Collaborative Encoder Framework for Unsupervised Clustering](https://arxiv.org/abs/1906.05173v1)
- [Continual and Multi-Task Architecture Search](https://arxiv.org/abs/1906.05226v1)
- [The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation](https://arxiv.org/abs/1804.09849v2)
- [Hierarchical Imitation and Reinforcement Learning](https://arxiv.org/abs/1803.00590v2)
- [Jacobian Policy Optimizations](https://arxiv.org/abs/1906.05437v1)
- [Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards](https://arxiv.org/abs/1906.05841v1)
- [A Bayesian Perspective on the Deep Image Prior](https://arxiv.org/abs/1904.07457v1)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://arxiv.org/abs/1906.05317v1)
- [Topology-Preserving Deep Image Segmentation](https://arxiv.org/abs/1906.05404v1)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://arxiv.org/abs/1906.05797v1)
- [Learning from Dialogue after Deployment: Feed Yourself, Chatbot!](https://arxiv.org/abs/1901.05415v4)
- [Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions](https://arxiv.org/abs/1806.02051v1)
- [Butterfly Transform: An Efficient FFT Based Neural Architecture Design](https://arxiv.org/abs/1906.02256v1)
- [A Survey on Deep Learning Architectures for Image-based Depth Reconstruction](https://arxiv.org/abs/1906.06113v1)
- [Neural Spline Flows](https://arxiv.org/abs/1906.04032v1)
- [NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language](https://arxiv.org/abs/1906.06187v1)
- [Deep learning for molecular design - a review of the state of the art](https://arxiv.org/abs/1903.04388v3)
- [Kernelized Capsule Networks](https://arxiv.org/abs/1906.03164v1)
- [Unsupervised Representation Learning of DNA Sequences](https://arxiv.org/abs/1906.03087v1)
- [Image Captioning: Transforming Objects into Words](https://arxiv.org/abs/1906.05963v1)
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://arxiv.org/abs/1906.06310v1)
- [Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks](https://arxiv.org/abs/1906.06086v1)
- [Fast Calculation of Probabilistic Power Flow: A Model-based Deep Learning Approach](https://arxiv.org/abs/1906.06017v1)
- [Machine Learning Approach to Earthquake Rupture Dynamics](https://arxiv.org/abs/1906.06250v1)
- [Stand-Alone Self-Attention in Vision Models](https://arxiv.org/abs/1906.05909v1)
- [Inverse Cooking: Recipe Generation from Food Images](https://arxiv.org/abs/1812.06164v2)
- [Large Scale Structure of Neural Network Loss Landscapes](https://arxiv.org/abs/1906.04724v1)
- [Generative Adversarial Networks: A Survey and Taxonomy](https://arxiv.org/abs/1906.01529v2) :star:
- [Accelerating Neural ODEs with Spectral Elements](https://arxiv.org/pdf/1906.07038.pdf)
- [Human Side of Tesla Autopilot:
Exploration of Functional Vigilance
in Real-World Human-Machine Collaboration](https://hcai.mit.edu/tesla-autopilot-human-side.pdf)
- [Stand-Alone Self-Attention in Vision Models](https://arxiv.org/abs/1906.05909v1)
- [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://arxiv.org/abs/1811.10092v2)
- [Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era](https://arxiv.org/abs/1906.06543v2)
- [Search on the Replay Buffer: Bridging Planning and Reinforcement Learning](https://arxiv.org/abs/1906.05253v1)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://arxiv.org/abs/1906.07155v1)
- [Task Agnostic Continual Learning via Meta Learning](https://arxiv.org/abs/1906.05201v1)
- [Show, Match and Segment: Joint Learning of Semantic Matching and Object Co-segmentation](https://arxiv.org/abs/1906.05857v1)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://arxiv.org/abs/1906.05797v1)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237v1) [[code](https://github.com/zihangdai/xlnet)] :star:
- [Weight Agnostic Neural Networks](https://arxiv.org/abs/1906.04358v1)
- [Model Compression by Entropy Penalized Reparameterization](https://arxiv.org/abs/1906.06624v1)
- [Analyzing the Structure of Attention in a Transformer Language Model](https://arxiv.org/abs/1906.04284v2)
- [PyRobot: An Open-source Robotics Framework for Research and Benchmarking](https://arxiv.org/abs/1906.08236v1) :star:
- [Explaining Reinforcement Learning to Mere Mortals: An Empirical Study](https://arxiv.org/abs/1903.09708v2)
- [EVDodge: Embodied AI For High-Speed Dodging On A Quadrotor Using Event Cameras](https://arxiv.org/abs/1906.02919v1)
- [Federated Learning for Emoji Prediction in a Mobile Keyboard](https://arxiv.org/abs/1906.04329v1)
- [Neural Volumes: Learning Dynamic Renderable Volumes from Images](https://arxiv.org/abs/1906.07751v1)
- [Discovery of Physics from Data: Universal Laws and Discrepancy Models](https://arxiv.org/abs/1906.07906v1)
- [Prune and Replace NAS](https://arxiv.org/pdf/1906.07528v1.pdf)
- [The Functional Neural Process](https://arxiv.org/pdf/1906.08324v1.pdf)
- [StoryGAN: A Sequential Conditional GAN for Story Visualization](https://arxiv.org/pdf/1812.02784v2.pdf)
- [Large Scale Structure of Neural Network Loss Landscapes](https://arxiv.org/abs/1906.04724v1)
- [Coupled Graph Neural Networks for Predicting the Popularity of Online Content](https://arxiv.org/abs/1906.09032v1)
- [PaperRobot: Incremental Draft Generation of Scientific Ideas](https://arxiv.org/abs/1905.07870v4)
- [Meta-learning of textual representations](https://arxiv.org/pdf/1906.08934v1.pdf)
- [Deep Set Prediction Networks](https://arxiv.org/abs/1906.06565v1)
- [Modern Deep Reinforcement Learning Algorithms](https://arxiv.org/abs/1906.10025v1)
- [Machine Learning Phase Transitions with a Quantum Processor](https://arxiv.org/pdf/1906.10155v1.pdf)
- [ORRB -- OpenAI Remote Rendering Backend](https://arxiv.org/abs/1906.11633v1)
- [Inducing Syntactic Trees from BERT Representations](https://arxiv.org/abs/1906.11511v1)
- [We Need No Pixels: Video Manipulation Detection Using Stream Descriptors](https://arxiv.org/abs/1906.08743v1)
- [End-to-End Visual Speech Recognition for Small-Scale Datasets](https://arxiv.org/abs/1904.01954v3)
- [Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis](https://arxiv.org/abs/1906.11384v1)
- [A Power Efficient Artificial Neuron Using Superconducting Nanowires](https://arxiv.org/ftp/arxiv/papers/1907/1907.00263.pdf) :star:

----------------------

05/2019

- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://arxiv.org/pdf/1905.11481v1.pdf) :star:
- [Defending Against Neural Fake News](https://arxiv.org/abs/1905.12616v1)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946v1) [[EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)] :star:
- [Graph Representations for Higher-Order Logic and Theorem Proving](https://arxiv.org/abs/1905.10006v1)
- [Speech2Face: Learning the Face Behind a Voice](https://arxiv.org/abs/1905.09773v1)
- [VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019](https://arxiv.org/abs/1905.11449v2)
- [Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting](https://arxiv.org/abs/1905.11395v1)
- [Video-to-Video Translation for Visual Speech Synthesis](https://arxiv.org/abs/1905.12043v1)
- [Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction](https://arxiv.org/abs/1905.08511v2)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://arxiv.org/abs/1903.12287v3) :star:
- [Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks](https://arxiv.org/abs/1905.11381v1)
- [Provably Powerful Graph Networks](https://arxiv.org/abs/1905.11136v1)
- [Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network](https://arxiv.org/abs/1904.12200v1)
- [Pre-training Graph Neural Networks](https://arxiv.org/abs/1905.12265v1)
- [Unsupervised Learning from Video with Deep Neural Embeddings](https://arxiv.org/abs/1905.11954v1)
- [Discrete Infomax Codes for Meta-Learning](https://arxiv.org/abs/1905.11656v1)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://arxiv.org/abs/1905.08233v1) :star:
- [Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation](https://arxiv.org/abs/1904.11102v1)
- [Adversarial Examples Are Not Bugs, They Are Features](https://arxiv.org/abs/1905.02175v2)
- [On Variational Bounds of Mutual Information](https://arxiv.org/abs/1905.06922v1)
- [How Does Batch Normalization Help Optimization?](https://arxiv.org/abs/1805.11604v5)
- [Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge](https://arxiv.org/abs/1905.11623v1)
- [Object Discovery with a Copy-Pasting GAN](https://arxiv.org/abs/1905.11369v1)
- [Towards Neural Decompilation](https://arxiv.org/abs/1905.08325v1)
- [Multi-Sample Dropout for Accelerated Training and Better Generalization](https://arxiv.org/abs/1905.09788v2)
- [Discrete Flows: Invertible Generative Models of Discrete Data](https://arxiv.org/abs/1905.10347v1)
- [Kervolutional Neural Networks](https://arxiv.org/abs/1904.03955v1)
- [Deep Scale-spaces: Equivariance Over Scale](https://arxiv.org/abs/1905.11697v1)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://arxiv.org/abs/1905.03197v1)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://arxiv.org/abs/1904.12787v2)
- [Embedding Human Knowledge in Deep Neural Network via Attention Map](https://arxiv.org/abs/1905.03540v1)
- [The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models](https://arxiv.org/abs/1806.06514v2)
- [Object Detection in 20 Years: A Survey](https://arxiv.org/abs/1905.05055v2) :star:
- [Brain-mediated Transfer Learning of Convolutional Neural Networks](https://arxiv.org/abs/1905.10037v1)
- [Evolving Rewards to Automate Reinforcement Learning](https://arxiv.org/abs/1905.07628v1)
- [FastSpeech: Fast, Robust and Controllable Text to Speech](https://arxiv.org/abs/1905.09263v4)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://arxiv.org/abs/1905.02249v1)
- [Magic: The Gathering is Turing Complete](https://arxiv.org/abs/1904.09828v2)
- [SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver](https://arxiv.org/abs/1905.12149v1) [[code](https://github.com/locuslab/SATNet)]
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://arxiv.org/abs/1905.02450v3)
- [Challenges of Real-World Reinforcement Learning](https://arxiv.org/abs/1904.12901v1)
- [Meta reinforcement learning as task inference](https://arxiv.org/abs/1905.06424v1)
- [Accelerating Proposal Generation Network for \\Fast Face Detection on Mobile Devices](https://arxiv.org/abs/1904.12094v1)
- [Go-Explore: a New Approach for Hard-Exploration Problems](https://arxiv.org/abs/1901.10995v2)
- [Finding Friend and Foe in Multi-Agent Games](https://arxiv.org/abs/1906.02330v1)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://arxiv.org/abs/1905.04899v1)
- [RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud](https://arxiv.org/abs/1905.08748v2)
- [Examining Structure of Word Embeddings with PCA](https://arxiv.org/abs/1906.00114v1)
- [Defending Against Neural Fake News](https://arxiv.org/abs/1905.12616v1)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://arxiv.org/abs/1906.00121v1)
- [Consensus Clustering: An Embedding Perspective, Extension and Beyond](https://arxiv.org/abs/1906.00120v1)
- [RAPID: Early Classification of Explosive Transients using Deep Learning](https://arxiv.org/abs/1904.00014v1)

---------------

04/2019

- [A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety](https://arxiv.org/abs/1904.02697v1)
- Analysing Mathematical Reasoning Abilities of Neural Models. https://arxiv.org/pdf/1904.01557v1.pdf [deepmind/mathematics_dataset](https://github.com/deepmind/mathematics_dataset)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://arxiv.org/abs/1904.02701v1)
- [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689v1)
- [Advancing NLP with Cognitive Language Processing Signals](https://arxiv.org/abs/1904.02682v1)
- [Visualizing Attention in Transformer-Based Language models](https://arxiv.org/abs/1904.02679v1)
- [Recommendations for Datasets for Source Code Summarization](https://arxiv.org/abs/1904.02660v1)
- [An End-to-End Baseline for Video Captioning](https://arxiv.org/abs/1904.02628v1)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://arxiv.org/abs/1904.02232v1)
- [Hyperbolic Image Embeddings](https://arxiv.org/abs/1904.02239v1)
- [PaintBot: A Reinforcement Learning Approach for Natural Media Painting](https://arxiv.org/abs/1904.02201v1)
- [Reducing BERT Pre-Training Time from 3 Days to 76 Minutes](https://arxiv.org/abs/1904.00962v1)
- [DAGCN: Dual Attention Graph Convolutional Networks](https://arxiv.org/abs/1904.02278v1)
- [T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor](https://arxiv.org/abs/1904.02698v1)
- [Computational Imaging for VLBI Image Reconstruction](https://arxiv.org/abs/1512.01413v2) (2016) :star:
- [AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning](https://arxiv.org/abs/1904.04153v1)
- [Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models](https://arxiv.org/abs/1904.04206v1)
- [ASAP: Architecture Search, Anneal and Prune](https://arxiv.org/abs/1904.04123v1)
- [Revisiting EmbodiedQA: A Simple Baseline and Beyond](https://arxiv.org/abs/1904.04166v1)
- [Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN](https://arxiv.org/abs/1904.04174v1)
- [A Closer Look at Few-shot Classification](https://arxiv.org/abs/1904.04232v1)
- [Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://arxiv.org/abs/1904.04195v1)
- [Streamlined Dense Video Captioning](https://arxiv.org/abs/1904.03870v1)
- [Analysing Mathematical Reasoning Abilities of Neural Models](https://arxiv.org/abs/1904.01557v1)
- [DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis](https://arxiv.org/abs/1904.01310v1)
- [Density Matching for Bilingual Word Embedding](https://arxiv.org/abs/1904.02343v1)
- [Characterizing the Social Interactions in the Artificial Bee Colony Algorithm](https://arxiv.org/abs/1904.04203v1)
- [MOTS: Multi-Object Tracking and Segmentation](https://arxiv.org/abs/1902.03604v2)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://arxiv.org/abs/1812.03506v2)
- [Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain](https://arxiv.org/abs/1904.04061v1)
- [Meta Filter Pruning to Accelerate Deep Convolutional Neural Networks](https://arxiv.org/abs/1904.03961v1)
- [Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations](https://arxiv.org/abs/1904.03959v1)
- [Feature Learning Viewpoint of AdaBoost and a New Algorithm](https://arxiv.org/abs/1904.03953v1)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://arxiv.org/abs/1901.02985v2) [[code](https://github.com/ocimk/autodeeplab)]
- [A Selective Overview of Deep Learning](https://arxiv.org/pdf/1904.05526v2.pdf)
- [Pun Generation with Surprise](https://arxiv.org/abs/1904.06828v1) :star:
- [Unsupervised Recurrent Neural Network Grammars](https://arxiv.org/abs/1904.03746v3)
- [Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation](https://arxiv.org/abs/1904.07209v1)
- [Tutorial: Safe and Reliable Machine Learning](https://arxiv.org/abs/1904.07204v1)
- [A Discussion on Solving Partial Differential Equations using Neural Networks](https://arxiv.org/abs/1904.07200v1)
- [Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving](https://arxiv.org/abs/1904.07189v1)
- [PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration](https://arxiv.org/abs/1901.09290v3)
- [Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth](https://arxiv.org/abs/1904.07087v1)
- [Optimising Trotter-Suzuki Decompositions for Quantum Simulation Using Evolutionary Strategies](https://arxiv.org/abs/1904.01336v2)
- [A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms](https://arxiv.org/abs/1904.06979v1)
- [The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent](https://arxiv.org/abs/1904.06963v1)
- [On the Performance of Differential Evolution for Hyperparameter Tuning](https://arxiv.org/abs/1904.06960v1)
- [GraphTSNE: A Visualization Technique for Graph-Structured Data](https://arxiv.org/abs/1904.06915v1)
- [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://arxiv.org/abs/1812.10352v2)
- [Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization](https://arxiv.org/abs/1904.06836v1)
- [An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search](https://arxiv.org/abs/1904.06834v1)
- [Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation](https://arxiv.org/abs/1904.02357v1)
- [Precise Detection in Densely Packed Scenes](https://arxiv.org/abs/1904.00853v1)
- [Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data](https://arxiv.org/abs/1904.07032v1)
- [Self-critical n-step Training for Image Captioning](https://arxiv.org/abs/1904.06861v1)
- [Pedestrian Detection in Thermal Images using Saliency Maps](https://arxiv.org/abs/1904.06859v1)
- [Mini-batch Serialization: CNN Training with Inter-layer Data Reuse](https://arxiv.org/abs/1810.00307v3)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://arxiv.org/abs/1904.06830v1)
- [Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces](https://arxiv.org/abs/1904.06827v1)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://arxiv.org/abs/1904.08653v1#)
- [Unsupervised Part-Based Disentangling of Object Shape and Appearance](https://arxiv.org/abs/1903.06946v2)
- [Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances](https://arxiv.org/abs/1904.10500v1)
- [A General Neural Network Architecture for Persistence Diagrams and Graph Classification](https://arxiv.org/abs/1904.09378v1)
- [Mercator: uncovering faithful hyperbolic embeddings of complex networks](https://arxiv.org/abs/1904.10814v1)
- [Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach](https://arxiv.org/abs/1904.10761v1)
- [The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification](https://arxiv.org/abs/1901.09126v2)
- [Variational approach to unsupervised learning](https://arxiv.org/abs/1904.10869v1)
- [A Novel Re-weighting Method for Connectionist Temporal Classification](https://arxiv.org/abs/1904.10619v1)
- [Language Models with Transformers](https://arxiv.org/abs/1904.09408v1)
- [LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds](https://arxiv.org/abs/1904.10037v1)
- [Text Classification Algorithms: A Survey](https://arxiv.org/abs/1904.08067v2)
- [The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs](https://arxiv.org/abs/1810.05993v2)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://arxiv.org/abs/1811.12359v3)
- [The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors](https://arxiv.org/abs/1904.10079v1)
- [Neural Logic Reinforcement Learning](https://arxiv.org/abs/1904.10729v1)
- [Segmenting the Future](https://arxiv.org/abs/1904.10666v1)
- [Holistic Large Scale Video Understanding](https://arxiv.org/abs/1904.11451v1)
- [Understanding the Behaviors of BERT in Ranking](https://arxiv.org/abs/1904.07531v3)
- [Machine learning for long-distance quantum communication](https://arxiv.org/abs/1904.10797v1)
- [Attention Augmented Convolutional Networks](https://arxiv.org/abs/1904.09925v1)
- [Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors](https://arxiv.org/abs/1904.10997)
- [Differentiable Pruning Method for Neural Networks](https://arxiv.org/abs/1904.10921v1)
- [BERTScore: Evaluating Text Generation with BERT](https://arxiv.org/abs/1904.09675v1) [[code](https://github.com/Tiiiger/bert_score)]
- [Out of the Box: A combined approach for handling occlusion in Human Pose Estimation](https://arxiv.org/abs/1904.11157v1)
- [RepPoints: Point Set Representation for Object Detection](https://arxiv.org/abs/1904.11490v1)
- [Generated Loss, Augmented Training, and Multiscale VAE](https://arxiv.org/abs/1904.10446v1)
- [Latent Variable Algorithms for Multimodal Learning and Sensor Fusion](https://arxiv.org/abs/1904.10450v1)
- [Unsupervised Deep Learning by Neighbourhood Discovery](https://arxiv.org/abs/1904.11567v1)
- [Unsupervised Data Augmentation](https://arxiv.org/abs/1904.12848v1)
- [HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks](https://arxiv.org/abs/1904.12846v1)
- [Dynamic Demand Prediction for Expanding Electric Vehicle Sharing Systems](https://arxiv.org/abs/1903.04051v2)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://arxiv.org/abs/1811.05577v2)
- [Beyond Gradient Descent for Regularized Segmentation Losses](https://arxiv.org/abs/1809.02322v2)
- [A Review of Modularization Techniques in Artificial Neural Networks](https://arxiv.org/abs/1904.12770v1)
- [Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network](https://arxiv.org/abs/1904.12769v1)
- [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760v1)
- [DAC: The Double Actor-Critic Architecture for Learning Options](https://arxiv.org/abs/1904.12691v1)
- [Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage](https://arxiv.org/abs/1904.12653v1)
- [Adversarial Examples: Opportunities and Challenges](https://arxiv.org/abs/1809.04790v2)
- [Provable limitations of deep learning](https://arxiv.org/abs/1812.06369v2) :star:
- [Evaluating Recurrent Neural Network Explanations](https://arxiv.org/abs/1904.11829v2)
- [Teaching AI, Ethics, Law and Policy](https://arxiv.org/abs/1904.12470v1)
- [Challenges and Pitfalls of Reproducing Machine Learning Artifacts](https://arxiv.org/abs/1904.12437v1)
- [Generating Artificial Data for Private Deep Learning](https://arxiv.org/abs/1803.03148v3)
- [Exploring Urban Air Quality with MAPS: Mobile Air Pollution Sensing](https://arxiv.org/abs/1904.12303v1)
- [Hierarchical Recurrent Neural Network for Video Summarization](https://arxiv.org/abs/1904.12251v1)
- [On Exact Computation with an Infinitely Wide Neural Net](https://arxiv.org/abs/1904.11955v1) :star:
- [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://arxiv.org/abs/1904.11544v1)
- [Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications](https://arxiv.org/abs/1904.12004v1)
- [Wave Physics as an Analog Recurrent Neural Network](https://arxiv.org/abs/1904.12831v1)
- [TVQA+: Spatio-Temporal Grounding for Video Question Answering](https://arxiv.org/abs/1904.11574v1)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://arxiv.org/abs/1904.01766v1)

-------------

03/2019

- [Predicting Research Trends From Arxiv](https://arxiv.org/abs/1903.02831v1)
- [Asynchronous Federated Optimization](https://arxiv.org/abs/1903.03934v2)
- [Improving Transparency of Deep Neural Inference Process](https://arxiv.org/abs/1903.05501v1)
- [GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks](https://arxiv.org/abs/1903.03894v1)
- [Unsupervised Discovery of Parts, Structure, and Dynamics](https://arxiv.org/abs/1903.05136v1)
- [Continual Learning via Neural Pruning](https://arxiv.org/abs/1903.04476v1)
- [Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data](https://arxiv.org/abs/1810.08553v3)
- [Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine](https://arxiv.org/abs/1804.00623v3)
- [High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors](https://arxiv.org/abs/1903.05898v1)
- [Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective](https://arxiv.org/abs/1903.05801v1)
- [AutoML @ NeurIPS 2018 challenge: Design and Results](https://arxiv.org/abs/1903.05263v2)
- [MILDNet: A Lightweight Single Scaled Deep Ranking Architecture](https://arxiv.org/abs/1903.00905v2)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://arxiv.org/abs/1712.05474v2)
- [VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning](https://arxiv.org/abs/1903.05757v1)
- [Natural Language Interaction with Explainable AI Models](https://arxiv.org/abs/1903.05720v1)
- [Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness](https://arxiv.org/abs/1903.05965v1)
- [Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning](https://arxiv.org/abs/1903.05942v1)
- [Lipschitz Generative Adversarial Nets](https://arxiv.org/abs/1902.05687v2)
- [Scalable Facial Image Compression with Deep Feature Reconstruction](https://arxiv.org/abs/1903.05921v1)
- [AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming](https://arxiv.org/abs/1810.00457v2)
- [MirrorGAN: Learning Text-to-image Generation by Redescription](https://arxiv.org/abs/1903.05854v1)
- [A Deep Patent Landscaping Model using Transformer and Graph Convolutional Network](https://arxiv.org/abs/1903.05823v1)
- [MILDNet: A Lightweight Single Scaled Deep Ranking Architecture](https://arxiv.org/abs/1903.00905v2)
- [Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning](https://arxiv.org/abs/1903.05766v1)
- [Reinforcement Learning and Inverse Reinforcement Learning with System 1 and System 2](https://arxiv.org/abs/1811.08549v2)
- [Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach](https://arxiv.org/abs/1903.05635v1)
- [Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis](https://arxiv.org/abs/1903.05628v1) [[code](https://github.com/HelenMao/MSGAN)]
- [RVOS: End-to-End Recurrent Network for Video Object Segmentation](https://arxiv.org/abs/1903.05612v1) [[code](https://imatge-upc.github.io/rvos/)]
- [Improving Transparency of Deep Neural Inference Process](https://arxiv.org/abs/1903.05501v1)
- [Visual Semantic Information Pursuit: A Survey](https://arxiv.org/abs/1903.05434v1)
- [Assessing Generalization in Deep Reinforcement Learning](https://arxiv.org/abs/1810.12282v2)
- [On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models](https://arxiv.org/abs/1903.06620v1)
- [Policy Distillation and Value Matching in Multiagent Reinforcement Learning](https://arxiv.org/abs/1903.06592v1)
- [Selective Kernel Networks](https://arxiv.org/abs/1903.06586v1)
- [Neuromorphic Hardware learns to learn](https://arxiv.org/abs/1903.06493v1)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://arxiv.org/abs/1903.06473v1)
- [Benchmarking Natural Language Understanding Services for building Conversational Agents](https://arxiv.org/abs/1903.05566v2)
- [A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks](https://arxiv.org/abs/1903.06464v1)
- [BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving](https://arxiv.org/abs/1903.06405v1)
- [Enabling Spike-based Backpropagation in State-of-the-art Deep Neural Network Architectures](https://arxiv.org/abs/1903.06379v1)
- [A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning](https://arxiv.org/abs/1903.06372v1)
- [ROS2Learn: a reinforcement learning framework for ROS 2](https://arxiv.org/abs/1903.06282v1)
- [Show, Translate and Tell](https://arxiv.org/abs/1903.06275v1)
- [Improving Neural Architecture Search Image Classifiers via Ensemble Learning](https://arxiv.org/abs/1903.06236v1)
- [Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition](https://arxiv.org/abs/1903.06150v1)
- [Autoregressive Models for Sequences of Graphs](https://arxiv.org/abs/1903.07299v1)
- [Human Activity Recognition for Edge Devices](https://arxiv.org/abs/1903.07563v1)
- [EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras](https://arxiv.org/abs/1903.07520v1)
- [Interpretable Deep Learning in Drug Discovery](https://arxiv.org/abs/1903.02788v2)
- [Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks](https://arxiv.org/abs/1903.07499v1)
- [Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation](https://arxiv.org/abs/1903.07424v1)
- [Neutron: An Implementation of the Transformer Translation Model and its Variants](https://arxiv.org/abs/1903.07402v1)
- [Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration](https://arxiv.org/abs/1903.07400v1)
- [Beyond Turing: Intelligent Agents Centered on the User](https://arxiv.org/abs/1901.06613v2)
- [Deep Reinforcement Learning with Feedback-based Exploration](https://arxiv.org/abs/1903.06151v1)
- [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](https://arxiv.org/abs/1903.06694v1)
- [The Missing Ingredient in Zero-Shot Neural Machine Translation](https://arxiv.org/abs/1903.07091v1)
- [Responses to a Critique of Artificial Moral Agents](https://arxiv.org/abs/1903.07021v1)
- [Inserting Videos into Videos](https://arxiv.org/abs/1903.06571v1)
- [Learning Correspondence from the Cycle-Consistency of Time](https://arxiv.org/abs/1903.07593v1)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://arxiv.org/abs/1903.08527v1)
- [CoCoNet: A Collaborative Convolutional Network](https://arxiv.org/abs/1901.09886v2)
- [A Principled Approach for Learning Task Similarity in Multitask Learning](https://arxiv.org/abs/1903.09109v1)
- [Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition](https://arxiv.org/abs/1811.03529v2)
- [Learning to Caption Images through a Lifetime by Asking Questions](https://arxiv.org/abs/1812.00235v3)
- [Megapixel Photon-Counting Color Imaging using Quanta Image Sensor](https://arxiv.org/abs/1903.09036v1)
- [Recent advances in conversational NLP : Towards the standardization of Chatbot building](https://arxiv.org/abs/1903.09025v1)
- [Localization of Unmanned Aerial Vehicles in Corridor Environments using Deep Learning](https://arxiv.org/abs/1903.09021v1)
- [Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer](https://arxiv.org/abs/1903.08953v1)
- [Dynamic Power Management for Neuromorphic Many-Core Systems](https://arxiv.org/abs/1903.08941v1)
- [Towards Characterizing Divergence in Deep Q-Learning](https://arxiv.org/abs/1903.08894v1)
- [Understanding Convolutional Neural Networks with Information Theory: An Initial Exploration](https://arxiv.org/abs/1804.06537v3)
- [LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving](https://arxiv.org/abs/1903.08701v1)
- [A Graph-structured Dataset for Wikipedia Research](https://arxiv.org/abs/1903.08597v1)
- [Artificial Intelligence : from Research to Application ; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2019)](https://arxiv.org/abs/1903.08495v1)
- [Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML](https://arxiv.org/abs/1903.08362v1)
- [Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art](https://arxiv.org/abs/1903.08356v1)
- [A Methodology for Search Space Reduction in QoS Aware Semantic Web Service Composition](https://arxiv.org/abs/1809.07045v2)
- [Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks](https://arxiv.org/abs/1810.05943v4)
- [Generative Models For Deep Learning with Very Scarce Data](https://arxiv.org/abs/1903.09030v1)
- [Progressive Sparse Local Attention for Video object detection](https://arxiv.org/abs/1903.09126v1)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://arxiv.org/abs/1903.07291v1)
- [Combining Model and Parameter Uncertainty in Bayesian Neural Networks](https://arxiv.org/abs/1903.07594v2)
- [Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction](https://arxiv.org/abs/1903.08642v1)
- [In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images](https://arxiv.org/abs/1903.08469v1)
- [IvaNet: Learning to jointly detect and segment objets with the help of Local Top-Down Modules](https://arxiv.org/abs/1903.07360v1)
- [Deep Models for Relational Databases](https://arxiv.org/abs/1903.09033v1)
- [Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype](https://arxiv.org/abs/1903.08500v1)
- [Natural Language Generation at Scale: A Case Study for Open Domain Question Answering](https://arxiv.org/abs/1903.08097v1)
- [Actor and Action Video Segmentation from a Sentence](https://arxiv.org/abs/1803.07485v1) :star:
- [DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction](https://arxiv.org/abs/1903.10481v1)
- [Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation](https://arxiv.org/abs/1903.10427v1)
- [Towards a framework for the evolution of artificial general intelligence](https://arxiv.org/abs/1903.10410v1)
- [On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning](https://arxiv.org/abs/1903.10404v1)
- [Learning-to-Learn Stochastic Gradient Descent with Biased Regularization](https://arxiv.org/abs/1903.10399v1)
- [Fine-tune BERT for Extractive Summarization](https://arxiv.org/abs/1903.10318v1)
- [Audio Caption: Listen and Tell](https://arxiv.org/abs/1902.09254v3)
- [MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning](https://arxiv.org/abs/1903.10258v1)
- [HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs](https://arxiv.org/abs/1903.04120v2)
- [Learning to Walk via Deep Reinforcement Learning](https://arxiv.org/abs/1812.11103v2)
- [Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks](https://arxiv.org/abs/1903.10195v1)
- [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://arxiv.org/abs/1901.06455v2)
- [Looking Fast and Slow: Memory-Guided Mobile Video Object Detection](https://arxiv.org/abs/1903.10172v1)
- [Continual Learning with Tiny Episodic Memories](https://arxiv.org/abs/1902.10486v2)
- [Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation](https://arxiv.org/abs/1903.10427v1)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://arxiv.org/abs/1812.01717v2)
- [NengoDL: Combining deep learning and neuromorphic modelling methods](https://arxiv.org/abs/1805.11144v3)
- [Autoregressive Policies for Continuous Control Deep Reinforcement Learning](https://arxiv.org/abs/1903.11524v1)
- [Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems](https://arxiv.org/abs/1903.11508v1)
- [A Sober Look at Neural Network Initializations](https://arxiv.org/abs/1903.11482v1)
- [Using Monolingual Data in Neural Machine Translation: a Systematic Study](https://arxiv.org/abs/1903.11437v1)
- [Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools](https://arxiv.org/abs/1903.11314v1)
- [Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods](https://arxiv.org/abs/1903.11260v1)
- [StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs](https://arxiv.org/abs/1807.11091v3)
- [GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving](https://arxiv.org/abs/1903.10955v2)
- [A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling](https://arxiv.org/abs/1808.01630v4)
- [Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning](https://arxiv.org/abs/1812.00971v2)
- [nuScenes: A multimodal dataset for autonomous driving](https://arxiv.org/abs/1903.11027v1)
- [DetNAS: Neural Architecture Search on Object Detection](https://arxiv.org/abs/1903.10979v1)
- [Differentiable Scene Graphs](https://arxiv.org/abs/1902.10200v2)
- [BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers](https://arxiv.org/abs/1902.10126v2)
- [The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation](https://arxiv.org/abs/1903.01602v1)
- [Random Language Model](https://arxiv.org/abs/1809.01201v2)
- [Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation](https://arxiv.org/abs/1903.10963v1)
- [Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI](https://arxiv.org/abs/1903.09240v1)
- [A Quantum Annealing-Based Approach to Extreme Clustering](https://arxiv.org/abs/1903.08256v1)
- [AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search](https://arxiv.org/abs/1903.11059v1)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://arxiv.org/abs/1812.01717v2)
- [TensorMask: A Foundation for Dense Object Segmentation](https://arxiv.org/abs/1903.12174v1)
- [3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles](https://arxiv.org/abs/1903.12152v1)
- [Fast video object segmentation with Spatio-Temporal GANs](https://arxiv.org/abs/1903.12161v1)
- [Resilient Combination of Complementary CNN and RNN Features for Text Classification through Attention and Ensembling](https://arxiv.org/abs/1903.12157v1)
- [Deep learning for molecular generation and optimization - a review of the state of the art](https://arxiv.org/abs/1903.04388v1)
- [Needle in a Haystack: A Framework for Seeking Small Objects in Big Datasets](https://arxiv.org/abs/1903.10019v1)


----------------------------

02/2019

- [The Hanabi Challenge: A New Frontier for AI Research](https://arxiv.org/abs/1902.00506v1) :star:
- [Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046v1)
- [Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey](https://arxiv.org/abs/1902.00753v1)
- [The Natural Language of Actions](https://arxiv.org/abs/1902.01119v1)
- [AlphaStar: An Evolutionary Computation Perspective](https://arxiv.org/abs/1902.01724v1)   :star:
- [Declarative Data Analytics: a Survey](https://arxiv.org/abs/1902.01304v1)
- [CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning](https://arxiv.org/abs/1902.00641v1)
- [A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases](https://arxiv.org/abs/1902.00663v1)
- [Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era](https://arxiv.org/abs/1902.00522v1)
- [Solving The Exam Scheduling Problems in Central Exams With Genetic Algorithms](https://arxiv.org/abs/1902.01360v1)
- [Adaptive Gradient Refinement for Adversarial Perturbation Generation](https://arxiv.org/abs/1902.01220v1)
- [Computational Optimal Transport](https://arxiv.org/abs/1803.00567v1)
- [Parameter-Efficient Transfer Learning for NLP](https://arxiv.org/abs/1902.01718v1)
- [End-to-End Open-Domain Question Answering with BERTserini](https://arxiv.org/abs/1902.01718v1)
- [Discovering Implicational Knowledge in Wikidata](https://arxiv.org/abs/1902.00916v1)
- [Graph Classification with Recurrent Variational Neural Networks](https://arxiv.org/abs/1902.02721v1)
- [A Simple Baseline for Bayesian Uncertainty in Deep Learning](https://arxiv.org/abs/1902.02476v1)
- [FDDB-360: Face Detection in 360-degree Fisheye Images](https://arxiv.org/abs/1902.02777v1)
- [Reversible GANs for Memory-efficient Image-to-Image Translation](https://arxiv.org/abs/1902.02729v1)
- [Review Conversational Reading Comprehension](https://arxiv.org/abs/1902.00821v1)
- [Distilling Policy Distillation](https://arxiv.org/abs/1902.02186v1)
- [Virtual Training for a Real Application: Accurate Object-Robot Relative Localization without Calibration](https://arxiv.org/abs/1902.02711v1)
- [BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning](https://arxiv.org/abs/1902.02671v1)
- [Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level](https://arxiv.org/abs/1902.02593v1)
- [Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers](https://arxiv.org/abs/1902.01474v1) :star:
- [Unsupervised Data Uncertainty Learning in Visual Retrieval Systems](https://arxiv.org/abs/1902.02586v1)
- [Understanding Chat Messages for Sticker Recommendation in Hike Messenger](https://arxiv.org/abs/1902.02704v1)
- [ML Health: Fitness Tracking for Production Models](https://arxiv.org/abs/1902.02808v1)
- [AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling](https://arxiv.org/abs/1902.02910v1)
- [Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046v1)
- [A Comprehensive Overview of Biometric Fusion](https://arxiv.org/abs/1902.02919v1)
- [Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning](https://arxiv.org/abs/1902.01378v1)
- [Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications](https://arxiv.org/abs/1902.03192v1)
- [Fourier Neural Networks: A Comparative Study](https://arxiv.org/abs/1902.03011v1)
- [Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target](https://arxiv.org/abs/1901.07510v2)
- [ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero](https://arxiv.org/abs/1902.04522v1)
- [Task2Vec: Task Embedding for Meta-Learning](https://arxiv.org/abs/1902.03545v1)
- [Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning](https://arxiv.org/abs/1808.03578v2)
- [Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving](https://arxiv.org/abs/1902.03442v1)
- [TensorSCONE: A Secure TensorFlow Framework using Intel SGX](https://arxiv.org/abs/1902.04413v1)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://arxiv.org/abs/1902.04502v1)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://arxiv.org/abs/1902.03748v1)
- [GET-AID: Visual Recognition of Human Rights Abuses via Global Emotional Traits](https://arxiv.org/abs/1902.03817v1)
- [Deep learning and face recognition: the state of the art](https://arxiv.org/abs/1902.03524v1)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://arxiv.org/abs/1902.04049v1)
- [NAIL: A General Interactive Fiction Agent](https://arxiv.org/abs/1902.04259v1)
- [Toward Unsupervised Text Content Manipulation](https://arxiv.org/abs/1901.09501v2)
- [Quantum Entanglement in Deep Learning Architectures](https://arxiv.org/abs/1803.09780v3) :star:
- [Image Processing in Quantum Computers](https://arxiv.org/abs/1812.11042v3)
- [ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning](https://arxiv.org/abs/1902.05009v1)
- [Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion](https://arxiv.org/abs/1812.08351v3)
- [Probabilistic Neural Architecture Search](https://arxiv.org/abs/1902.05116v1)
- [VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control](https://arxiv.org/abs/1802.00265v4)
- [Pseudo-extended Markov chain Monte Carlo](https://arxiv.org/abs/1708.05239v2)
- [Task2Vec: Task Embedding for Meta-Learning](https://arxiv.org/abs/1902.03545v1)
- [Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey](https://arxiv.org/abs/1812.07127v3)
- [Multi-task learning to improve natural language understanding](https://arxiv.org/abs/1812.06876v2)
- [Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic](https://arxiv.org/abs/1902.05772v1)
- [AutoQB: AutoML for Network Quantization and Binarization on Mobile Devices](https://arxiv.org/abs/1902.05690v1)
- [Crime Analysis using Open Source Information](https://arxiv.org/abs/1902.05684v1)
- [Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions](https://arxiv.org/abs/1902.05655v1)
- [WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection](https://arxiv.org/abs/1902.05625v1)
- [GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images](https://arxiv.org/abs/1902.05611v1)
- [Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting](https://arxiv.org/abs/1810.07733v2)
- [Real-world Anomaly Detection in Surveillance Videos](https://arxiv.org/abs/1801.04264v3)
- [Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions](https://arxiv.org/abs/1811.10652v2)
- [Transfer Learning for Sequence Labeling Using Source Model and Target Data](https://arxiv.org/abs/1902.05309v1)
- [Experimental Implementation of a Quantum Autoencoder via Quantum Adders](https://arxiv.org/abs/1807.10643v2)
- [Some Interesting Features of Memristor CNN](https://arxiv.org/abs/1902.05167v1) :star:
- [Competitive Experience Replay](https://arxiv.org/abs/1902.00528v3)
- [Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond](https://arxiv.org/abs/1902.05908v1)
- [Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent](https://arxiv.org/abs/1902.06720v1)
- [Contextual Word Representations: A Contextual Introduction](https://arxiv.org/abs/1902.06006v2)
- [Deep Learning for Image Super-resolution: A Survey](https://arxiv.org/abs/1902.06068v1)
- [Fast Efficient Hyperparameter Tuning for Policy Gradients](https://arxiv.org/abs/1902.06583v1)
- [Seven Myths in Machine Learning Research](https://arxiv.org/abs/1902.06789v1)
- [WIDER Face and Pedestrian Challenge 2018: Methods and Results](https://arxiv.org/abs/1902.06854v1)
- [Towards Explainable AI: Significance Tests for Neural Networks](https://arxiv.org/abs/1902.06021v1)
- [Investigating Generalisation in Continuous Deep Reinforcement Learning](https://arxiv.org/abs/1902.07015v1)
- [CrossNorm: Normalization for Off-Policy TD Reinforcement Learning](https://arxiv.org/abs/1902.05605v1)
- [Depthwise Convolution is All You Need for Learning Multiple Visual Domains](https://arxiv.org/abs/1902.00927v2)
- [CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model](https://arxiv.org/abs/1902.06423v1)
- [Parenting: Safe Reinforcement Learning from Human Input](https://arxiv.org/abs/1902.06766v1)
- [Learned In Speech Recognition: Contextual Acoustic Word Embeddings](https://arxiv.org/abs/1902.06833v1)
- [Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning](https://arxiv.org/abs/1902.06744v1)
- [Towards Hardware Implementation of Neural Network-based Communication Algorithms](https://arxiv.org/abs/1902.06939v1)
- [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) [[code](https://github.com/openai/gpt-2)]  :star:
- [SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color](https://arxiv.org/abs/1902.06838v1)
- [Random Search and Reproducibility for Neural Architecture Search](https://arxiv.org/abs/1902.07638v1)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://arxiv.org/abs/1902.07669v2)
- [Transfusion: Understanding Transfer Learning with Applications to Medical Imaging](https://arxiv.org/abs/1902.07208v1)
- [An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU](https://arxiv.org/abs/1902.07385v1)
- [Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)](https://arxiv.org/abs/1902.07327v1)
- [POINTCLEANNET: Learning to Denoise and Remove Outliers from Dense Point Clouds](https://arxiv.org/abs/1901.01060v2)
- [Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent](https://arxiv.org/abs/1902.06720v1)
- [GSLAM: A General SLAM Framework and Benchmark](https://arxiv.org/abs/1902.07995v1)
- [The Devil is in the Decoder: Classification, Regression and GANs](https://arxiv.org/abs/1707.05847v3)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://arxiv.org/abs/1802.02627v4)
- [Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices](https://arxiv.org/abs/1902.07891v1)
- [Cloud-Based Autonomous Indoor Navigation: A Case Study](https://arxiv.org/abs/1902.08052v1)
- [Visualizing the Loss Landscape of Neural Nets](https://arxiv.org/abs/1712.09913v3)
- [Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection](https://arxiv.org/abs/1809.05884v2)
- [Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning](https://arxiv.org/abs/1902.06744v1)
- [Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank](https://arxiv.org/abs/1902.06285v1)
- [Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers](https://arxiv.org/abs/1902.07776v1)
- [Innateness, AlphaZero, and Artificial Intelligence](https://arxiv.org/abs/1801.05667v1)
- [Universal Deep Neural Network Compression](https://arxiv.org/abs/1802.02271v2)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://arxiv.org/abs/1902.06162v1)
- [Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic](https://arxiv.org/abs/1902.05772v1)
- [Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer](https://arxiv.org/abs/1809.02444v1)
- [Multimodal Grounding for Sequence-to-Sequence Speech Recognition](https://arxiv.org/abs/1811.03865v2)
- [A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation](https://arxiv.org/abs/1902.07971v1)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://arxiv.org/abs/1902.07880v1)
- [Evaluating the Search Phase of Neural Architecture Search](https://arxiv.org/abs/1902.08142v1)
- [Topology of Learning in Artificial Neural Networks](https://arxiv.org/abs/1902.08160v1)
- [Evolutionary Neural AutoML for Deep Learning](https://arxiv.org/abs/1902.06827v1)
- [Simplifying Graph Convolutional Networks](https://arxiv.org/abs/1902.07153v1)
- [Adversarial Generation of Time-Frequency Features with application in audio synthesis](https://arxiv.org/abs/1902.04072v1)
- [Latent Translation: Crossing Modalities by Bridging Generative Models](https://arxiv.org/abs/1902.08261v1)
- [Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling](https://arxiv.org/abs/1902.08295v1) :star:
- [E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction](https://arxiv.org/abs/1902.08329v1)
- [Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment](https://arxiv.org/abs/1902.08231v1)
- [Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data](https://arxiv.org/abs/1902.08321v1)
- [Distributionally Robust Reinforcement Learning](https://arxiv.org/abs/1902.08708v1)
- [The Multi-Lane Capsule Network (MLCN)](https://arxiv.org/abs/1902.08431v1)
- [Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks](https://arxiv.org/abs/1902.09355v1)
- [Pretraining-Based Natural Language Generation for Text Summarization](https://arxiv.org/abs/1902.09243v1)
- [GANSynth: Adversarial Neural Audio Synthesis](https://arxiv.org/abs/1902.08710v1)
- [Latent Translation: Crossing Modalities by Bridging Generative Models](https://arxiv.org/abs/1902.08261v1)
- [Scalable Hyperbolic Recommender Systems](https://arxiv.org/abs/1902.08648v1)
- [Dual Attention Networks for Visual Reference Resolution in Visual Dialog](https://arxiv.org/abs/1902.09368v1)
- [Improving Robustness of Machine Translation with Synthetic Noise](https://arxiv.org/abs/1902.09508v1)
- [A Survey of Crowdsourcing in Medical Image Analysis](https://arxiv.org/abs/1902.09159v1)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://arxiv.org/abs/1902.09063v1)
- [FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation](https://arxiv.org/abs/1902.09513v1)
- [DDFlow: Learning Optical Flow with Unlabeled Data Distillation](https://arxiv.org/abs/1902.09145v1)
- [Artificial Intelligence for Prosthetics - challenge solutions](https://arxiv.org/abs/1902.02441v1)
- [Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era](https://arxiv.org/abs/1902.10078v1)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://arxiv.org/abs/1902.09635v1)
- [Diagnosing Bottlenecks in Deep Q-learning Algorithms](https://arxiv.org/abs/1902.10250v1)
- [TraVeLGAN: Image-to-image Translation by Transformation Vector Learning](https://arxiv.org/abs/1902.09631v1)
- [Data augmentation using learned transforms for one-shot medical image segmentation](https://arxiv.org/abs/1902.09383v1)
- [Multi-Scale Quasi-RNN for Next Item Recommendation](https://arxiv.org/abs/1902.09849v1)
- [Efficient Learning for Deep Quantum Neural Networks](https://arxiv.org/abs/1902.10445v1)
- [Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions](https://arxiv.org/abs/1902.10402v1)
- [Adaptive Caching via Deep Reinforcement Learning](https://arxiv.org/abs/1902.10301v1)
- [TensorMap: Lidar-Based Topological Mapping and Localization via Tensor Decompositions](https://arxiv.org/abs/1902.10226v1)
- [Coloring Big Graphs with AlphaGoZero](https://arxiv.org/abs/1902.10162v2) :star:
- [An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models](https://arxiv.org/abs/1902.10547v1) :star:
- [The Termination Critic](https://arxiv.org/abs/1902.09996v1)
- [How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection](https://arxiv.org/abs/1902.10339v1)
- [Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies](https://arxiv.org/abs/1902.10140v1)
- [DeepLO: Geometry-Aware Deep LiDAR Odometry](https://arxiv.org/abs/1902.10562v1)
- [Improving Missing Data Imputation with Deep Generative Models](https://arxiv.org/abs/1902.10666v1)
- [Accelerating Self-Play Learning in Go](https://arxiv.org/abs/1902.10565v2)
- [Reducing Artificial Neural Network Complexity: A Case Study on Exoplanet Detection](https://arxiv.org/abs/1902.10385v1)
- [Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU](https://arxiv.org/abs/1902.10194v1)
- [Attention is not Explanation](https://arxiv.org/abs/1902.10186v1)
- [Implicit Kernel Learning](https://arxiv.org/abs/1902.10214v1)
- [Architecting Dependable Learning-enabled Autonomous Systems: A Survey](https://arxiv.org/abs/1902.10590v1)
- [On Constrained Open-World Probabilistic Databases](https://arxiv.org/abs/1902.10677v1)
- [Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning](https://arxiv.org/abs/1902.10322v1)
- [Learning to Generate Questions by Learning What not to Generate](https://arxiv.org/abs/1902.10418v1)
- [Domain-Constrained Advertising Keyword Generation](https://arxiv.org/abs/1902.10374v1)
- [Multilingual Neural Machine Translation with Knowledge Distillation](https://arxiv.org/abs/1902.10461v2)
- [StyleRemix: An Interpretable Representation for Neural Image Style Transfer](https://arxiv.org/abs/1902.10425v1)
- [Provable Guarantees for Gradient-Based Meta-Learning](https://arxiv.org/abs/1902.10644v1)
- [Deep Adversarial Network Alignment](https://arxiv.org/abs/1902.10307v1)
- [Multi-task hypernetworks](https://arxiv.org/abs/1902.10404v1)
- [Atomistic structure learning](https://arxiv.org/abs/1902.10501v1)

-------------

01/2019

- [Theory of Minds: Understanding Behavior in Groups Through Inverse Planning](https://arxiv.org/abs/1901.06085v1)
- [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596v1)
- [Face Recognition: A Novel Multi-Level Taxonomy based Survey](https://arxiv.org/abs/1901.00713v1)
- [DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation](https://arxiv.org/abs/1801.02805v2) [[code](https://github.com/lexfridman/deeptraffic)]
- [Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning](https://arxiv.org/abs/1901.01325v1)
- [Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals](https://arxiv.org/abs/1809.09318v4)
- [A Critical Investigation of Deep Reinforcement Learning for Navigation](https://arxiv.org/abs/1802.02274v2)
- [Adversarial Examples versus Cloud-based Detectors: A Black-box Empirical Study](https://arxiv.org/abs/1901.01223v1)
- [Sheaves: A Topological Approach to Big Data](https://arxiv.org/abs/1901.01341v1)
- [AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection](https://arxiv.org/abs/1901.01342v1)
- [Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning](https://arxiv.org/abs/1901.01703v1)
- [Text2Scene: Generating Compositional Scenes from Textual Descriptions](https://arxiv.org/abs/1809.01110v2)
- [Addressing Objects and Their Relations: The Conversational Entity Dialogue Model](https://arxiv.org/abs/1901.01466v1)
- [Designing for Democratization: Introducing Novices to Artificial Intelligence Via Maker Kits](https://arxiv.org/abs/1805.10723v3)
- [Elimination of All Bad Local Minima in Deep Learning](https://arxiv.org/abs/1901.00279v1)
- [Location-Centered House Price Prediction: A Multi-Task Learning Approach](https://arxiv.org/abs/1901.01774v1)
- [Credit Assignment Techniques in Stochastic Computation Graphs](https://arxiv.org/abs/1901.01761v1)
- [Human Pose Estimation with Spatial Contextual Information](https://arxiv.org/abs/1901.01760v1)
- [Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination](https://arxiv.org/abs/1901.01751v1)
- [Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search](https://arxiv.org/abs/1901.01074v2)
- [Quaternion Recurrent Neural Networks](https://arxiv.org/abs/1806.04418v3)
- [Ten ways to fool the masses with machine learning](https://arxiv.org/abs/1901.01686v1)
- [Unrepresentative video data: A review and evaluation](https://arxiv.org/abs/1811.11815v2)
- [Automated Pruning for Deep Neural Network Compression](https://arxiv.org/abs/1712.01721v2)
- [Learning Combinations of Activation Functions](https://arxiv.org/abs/1801.09403v2)
- [Bandwidth Reduction using Importance Weighted Pruning on Ring AllReduce](https://arxiv.org/abs/1901.01544v1)
- [Exploring applications of deep reinforcement learning for real-world autonomous driving systems](https://arxiv.org/abs/1901.01536v1) :star:
- [RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials](https://arxiv.org/abs/1901.01535v1)
- [Gauges, Loops, and Polynomials for Partition Functions of Graphical Models](https://arxiv.org/abs/1811.04713v4)
- [Channel Locality Block: A Variant of Squeeze-and-Excitation](https://arxiv.org/abs/1901.01493v1)
- [What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning](https://arxiv.org/abs/1901.01492v1)
- [Transferable Adversarial Attacks for Image and Video Object Detection](https://arxiv.org/abs/1811.12641v4)
- [LanczosNet: Multi-Scale Deep Graph Convolutional Networks](https://arxiv.org/abs/1901.01484v1)
- [Rethinking the Artificial Neural Networks: A Mesh of Subnets with a Central Mechanism for Storing and Predicting the Data](https://arxiv.org/abs/1901.01462v1)
- [Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud](https://arxiv.org/abs/1809.03428v3)
- [The ISTI Rapid Response on Exploring Cloud Computing 2018](https://arxiv.org/abs/1901.01331v1)
- [FIGR: Few-shot Image Generation with Reptile](https://arxiv.org/abs/1901.02199v1)
- [Multi-stream CNN based Video Semantic Segmentation for Automated Driving](https://arxiv.org/abs/1901.02511v1)
- [Multi-style Generative Reading Comprehension](https://arxiv.org/abs/1901.02262v1)
- [GILT: Generating Images from Long Text](https://arxiv.org/abs/1901.02404v1)
- [On the Capabilities and Limitations of Reasoning for Natural Language Understanding](https://arxiv.org/abs/1901.02522v1)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860v1)
- [Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera](https://arxiv.org/abs/1901.02571v1)
- [Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems](https://arxiv.org/abs/1901.02490v1)
- [The Lingering of Gradients: How to Reuse Gradients over Time](https://arxiv.org/abs/1901.02871v1)
- [Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks](https://arxiv.org/abs/1901.02184v1)
- [Asteroids Detection Technique: Classic "Blink" An Automated Approch](https://arxiv.org/abs/1901.02542v1)
- [Machine learning topological phases in real space](https://arxiv.org/abs/1901.01963v1)
- [Dirichlet Variational Autoencoder](https://arxiv.org/abs/1901.02739v1)
- [Recurrent Control Nets for Deep Reinforcement Learning](https://arxiv.org/abs/1901.01994v1)
- [Spherical CNNs on Unstructured Grids](https://arxiv.org/abs/1901.02039v1)
- [Risk-Aware Active Inverse Reinforcement Learning](https://arxiv.org/abs/1901.02161v1)
- [Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks](https://arxiv.org/abs/1901.02671v1)
- [An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents](https://arxiv.org/abs/1812.07069v1)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://arxiv.org/abs/1901.02985v1)
- [Revealing interpretable object representations from human behavior](https://arxiv.org/abs/1901.02915v1)
- [Deep Learning for Human Affect Recognition: Insights and New Developments](https://arxiv.org/abs/1901.02884v1)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860v1) [[code](https://github.com/kimiyoung/transformer-xl)]
- [Exploring Weight Symmetry in Deep Neural Networks](https://arxiv.org/abs/1812.11027v2)
- [Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models](https://arxiv.org/abs/1901.03209v1)
- [Motion Perception in Reinforcement Learning with Dynamic Objects](https://arxiv.org/abs/1901.03162v1)
- [Brief Review of Computational Intelligence Algorithms](https://arxiv.org/abs/1901.00983v2) :star:
- [PVSS: A Progressive Vehicle Search System for Video Surveillance Networks](https://arxiv.org/abs/1901.03062v1)
- [Learning Face Age Progression: A Pyramid Architecture of GANs](https://arxiv.org/abs/1711.10352v4)
- [Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications](https://arxiv.org/abs/1805.07687v4)
- [PFML-based Semantic BCI Agent for Game of Go Learning and Prediction](https://arxiv.org/abs/1901.02999v1)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://arxiv.org/abs/1901.02985v1)
- [Accelerated Flow for Probability distributions](https://arxiv.org/abs/1901.03317v1)
- [On Finding Gray Pixels](https://arxiv.org/abs/1901.03198v1)
- [RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free](https://arxiv.org/abs/1901.03353v1)
- [Motion Perception in Reinforcement Learning with Dynamic Objects](https://arxiv.org/abs/1901.03162v1)
- [Deep Learning for Anomaly Detection: A Survey](https://arxiv.org/abs/1901.03407v1)
- [An investigation of model-free planning](https://arxiv.org/abs/1901.03559v1)
- [Dialog System Technology Challenge 7](https://arxiv.org/abs/1901.03461v1)
- [On Event Causality Detection in Tweets](https://arxiv.org/abs/1901.03526v1)
- [A Biologically Inspired Visual Working Memory for Deep Networks](https://arxiv.org/abs/1901.03665v1)
- [Agent-Agnostic Human-in-the-Loop Reinforcement Learning](https://arxiv.org/abs/1701.04079v1) :star:
- [Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes](https://arxiv.org/abs/1901.03796v1)
- [Using Scene Graph Context to Improve Image Generation](https://arxiv.org/abs/1901.03762v1)
- [Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving](https://arxiv.org/abs/1901.03912v1)
- [Summarization and Visualization of Large Volumes of Broadcast Video Data](https://arxiv.org/abs/1901.03842v1)
- [Trace-back Along Capsules and Its Application on Semantic Segmentation](https://arxiv.org/abs/1901.02920v1)
- [LiFF: Light Field Features in Scale and Depth](https://arxiv.org/abs/1901.03916v1)
- [Human few-shot learning of compositional instructions](https://arxiv.org/abs/1901.04587v1)
- [Self-Supervised Deep Active Accelerated MRI](https://arxiv.org/abs/1901.04547v1)
- [Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition](https://arxiv.org/abs/1901.04889v1)
- [Toward Explainable Fashion Recommendation](https://arxiv.org/abs/1901.04870v1)
- [A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection](https://arxiv.org/abs/1901.04787v1)
- [DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion](https://arxiv.org/abs/1901.04780v1)
- [Transfer Learning for Prosthetics Using Imitation Learning](https://arxiv.org/abs/1901.04772v1)
- [Approaching Ethical Guidelines for Data Scientists](https://arxiv.org/abs/1901.04824v1)
- [Identifying and Correcting Label Bias in Machine Learning](https://arxiv.org/abs/1901.04966v1)
- [Low Precision Constant Parameter CNN on FPGA](https://arxiv.org/abs/1901.04969v1)
- [Global-to-local Memory Pointer Networks for Task-Oriented Dialogue](https://arxiv.org/abs/1901.04713v1)
- [Tango: A Deep Neural Network Benchmark Suite for Various Accelerators](https://arxiv.org/abs/1901.04987v1)
- [Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges](https://arxiv.org/abs/1901.04730v1)
- [AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning](https://arxiv.org/abs/1901.04615v1)
- [The Limitations of Adversarial Training and the Blind-Spot Attack](https://arxiv.org/abs/1901.04684v1)
- [ALiPy: Active Learning in Python](https://arxiv.org/abs/1901.03802v1)
- [Hybrid Recommender Systems: A Systematic Literature Review](https://arxiv.org/abs/1901.03888v1)
- [Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding](https://arxiv.org/abs/1810.02338v2)
- [Recombination of Artificial Neural Networks](https://arxiv.org/abs/1901.03900v1)
- [Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://arxiv.org/abs/1901.04604v1)
- [A deep learning approach to solar-irradiance forecasting in sky-videos](https://arxiv.org/abs/1901.04881v1)
- [Interpretable machine learning: definitions, methods, and applications](https://arxiv.org/abs/1901.04592v1)
- [Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation](https://arxiv.org/abs/1901.04949v1)
- [TensorFlow.js: Machine Learning for the Web and Beyond](https://arxiv.org/pdf/1901.05350v1.pdf)
- [Attentive Neural Processes](https://arxiv.org/abs/1901.05761v1) :star:
- [Convolutional Neural Networks combined with Runge-Kutta Methods](https://arxiv.org/abs/1802.08831v6)
- [Learning from Dialogue after Deployment: Feed Yourself, Chatbot!](https://arxiv.org/abs/1901.05415v1)
- [Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails](https://arxiv.org/abs/1901.05599v1)
- [VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control](https://arxiv.org/abs/1802.00265v4)
- [AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving](https://arxiv.org/abs/1901.05808v1)
- [Artificial Neural Networks](https://arxiv.org/abs/1901.05639v1)
- [Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference](https://arxiv.org/abs/1901.05906v1)
- [Assessing BERT's Syntactic Abilities](https://arxiv.org/abs/1901.05287v1)
- [EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search](https://arxiv.org/abs/1901.05884v1)
- [UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks](https://arxiv.org/abs/1901.05880v1)
- [Primitive-based 3D Building Modeling, Sensor Simulation, and Estimation](https://arxiv.org/abs/1901.05554v1)
- [The Oracle of DLphi](https://arxiv.org/abs/1901.05744v1)
- [Recent Advances in Autoencoder-Based Representation Learning](https://arxiv.org/abs/1812.05069v1)
- [Towards Building the Semantic Map from a Monocular Camera with a Multi-task Network](https://arxiv.org/abs/1901.05807v1)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://arxiv.org/abs/1901.05103v1)
- [Auto-Retoucher(ART) - A framework for Background Replacement and Image Editing](https://arxiv.org/abs/1901.03954v1)
- [Ensemble Feature for Person Re-Identification](https://arxiv.org/abs/1901.05798v1)
- [Machine Learning Techniques for Brand-Influencer Matchmaking on the Instagram Social Network](https://arxiv.org/abs/1901.05949v1)
- [Predicting Variable Types in Dynamically Typed Programming Languages](https://arxiv.org/abs/1901.05138v1)
- [Light-Field for RF](https://arxiv.org/abs/1901.03953v1)
- [Passage Re-ranking with BERT](https://arxiv.org/abs/1901.04085v2)
- [Learning to Design Circuits](https://arxiv.org/abs/1812.02734v3)
- [Video-Based Pedestrian Attribute Recognition](https://arxiv.org/abs/1901.05742v1)
- [Genetic Algorithms and the Traveling Salesman Problem a historical Review](https://arxiv.org/abs/1901.05737v1)
- [Evolving embodied intelligence from materials to machines](https://arxiv.org/abs/1901.05704v1)
- [CodeX: Bit-Flexible Encoding for Streaming-based FPGA Acceleration of DNNs](https://arxiv.org/abs/1901.05582v1)
- [Survey of Bayesian Networks Applications on Unmanned Intelligent Autonomous Vehicles](https://arxiv.org/abs/1901.05517v1)
- [Deep learning-based electroencephalography analysis: a systematic review](https://arxiv.org/abs/1901.05498v1)
- [Evolutionarily-Curated Curriculum Learning for Deep Reinforcement Learning Agents](https://arxiv.org/abs/1901.05431v1)
- [Optimization Models for Machine Learning: A Survey](https://arxiv.org/abs/1901.05331v1)
- [Solar-Sail Trajectory Design of Multiple Near Earth Asteroids Exploration Based on Deep Neural Network](https://arxiv.org/abs/1901.02172v2)
- [Block Argumentation](https://arxiv.org/abs/1901.06378v1)
- [Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions](https://arxiv.org/abs/1901.00246v2)
- [NeuNetS: An Automated Synthesis Engine for Neural Network Design](https://arxiv.org/abs/1901.06261v1)
- [WALL-E: An Efficient Reinforcement Learning Research Framework](https://arxiv.org/abs/1901.06086v1)
- [Learning Dexterous In-Hand Manipulation](https://arxiv.org/abs/1808.00177v5)
- [Training Neural Networks with Local Error Signals](https://arxiv.org/abs/1901.06656v1)
- [Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning](https://arxiv.org/abs/1901.07517v1)
- [cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291v1)
- [DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features](https://arxiv.org/abs/1901.07223v1)
- [Improved Selective Refinement Network for Face Detection](https://arxiv.org/abs/1901.06651v1)
- [Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks](https://arxiv.org/abs/1901.06523v1)
- [Design of Real-time Semantic Segmentation Decoder for Automated Driving](https://arxiv.org/abs/1901.06580v1)
- [Causal Reasoning from Meta-reinforcement Learning](https://arxiv.org/abs/1901.08162v1)
- [Automated Essay Scoring based on Two-Stage Learning](https://arxiv.org/abs/1901.07744v1)
- [Decoupled Greedy Learning of CNNs](https://arxiv.org/abs/1901.08164v1)
- [Open-ended Learning in Symmetric Zero-sum Games](https://arxiv.org/abs/1901.08106v1)
- [Learning Neurosymbolic Generative Models via Program Synthesis](https://arxiv.org/abs/1901.08565v1)
- [Emotion Detection and Analysis on Social Media](https://arxiv.org/abs/1901.08458v1)
- [Large-Batch Training for LSTM and Beyond](https://arxiv.org/abs/1901.08256v1)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://arxiv.org/abs/1901.08573v1)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://arxiv.org/abs/1901.08149v1)
- [QGAN: Quantized Generative Adversarial Networks](https://arxiv.org/abs/1901.08263v1)
- [Explainable Failure Predictions with RNN Classifiers based on Time Series Data](https://arxiv.org/abs/1901.08554v1)
- [Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search](https://arxiv.org/abs/1901.07261v2)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://arxiv.org/abs/1901.08149v1)
- [Learning Neurosymbolic Generative Models via Program Synthesis](https://arxiv.org/abs/1901.08565v1)
- [Three principles of data science: predictability, computability, and stability (PCS)](https://arxiv.org/abs/1901.08152v1)
- [Understanding Geometry of Encoder-Decoder CNNs](https://arxiv.org/abs/1901.07647v1)
- [PuppetGAN: Transferring Disentangled Properties from Synthetic to Real Images](https://arxiv.org/abs/1901.10024v1)
- [Finding Archetypal Spaces for Data Using Neural Networks](https://arxiv.org/abs/1901.09078v1)
- [Generating Textual Adversarial Examples for Deep Learning Models: A Survey](https://arxiv.org/abs/1901.06796v2)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://arxiv.org/abs/1901.09960v1)
- [Non-computability of human intelligence](https://arxiv.org/abs/1810.06985v4)
- [Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification](https://arxiv.org/abs/1901.10112v1)
- [DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks](https://arxiv.org/abs/1901.09229v1)
- [Hierarchically Clustered Representation Learning](https://arxiv.org/abs/1901.09906v1)
- [Close to Human Quality TTS with Transforme](https://arxiv.org/abs/1809.08895v2)
- [Lyapunov-based Safe Policy Optimization for Continuous Control](https://arxiv.org/abs/1901.10031v1)
- [A deep learning framework for assessment of quality of rehabilitation exercises](https://arxiv.org/abs/1901.10435v1)
- [Limitations of Assessing Active Learning Performance at Runtime](https://arxiv.org/abs/1901.10338v1)
- [Evaluating Word Embedding Models: Methods and Experimental Results](https://arxiv.org/abs/1901.09785v2)
- [Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery](https://arxiv.org/abs/1901.10077v1)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://arxiv.org/abs/1901.08746v2)
- [Generalized Label Propagation Methods for Semi-Supervised Learning](https://arxiv.org/abs/1901.09993v1)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://arxiv.org/abs/1901.10430v1)
- [FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals](https://arxiv.org/abs/1901.02212v1)
- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882v2)
- [A BERT Baseline for the Natural Questions](https://arxiv.org/abs/1901.08634v1)
- [MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns](https://arxiv.org/abs/1901.10312v1)
- [Fair Regression for Health Care Spending](https://arxiv.org/abs/1901.10566v1)
- [Revisiting Self-Supervised Visual Representation Learning](https://arxiv.org/abs/1901.09005v1)
- [The Value Function Polytope in Reinforcement Learning](https://arxiv.org/abs/1901.11524v1)
- [A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms](https://arxiv.org/abs/1901.10912v1)
- [Go-Explore: a New Approach for Hard-Exploration Problems](https://arxiv.org/abs/1901.10995v1)
- [DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals](https://arxiv.org/abs/1901.11082v1)
- [The Evolved Transformer](https://arxiv.org/abs/1901.11117v1)
- [HyperGAN: A Generative Model for Diverse, Performant Neural Networks](https://arxiv.org/abs/1901.11058v1)
- [Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop](https://arxiv.org/abs/1901.10619v1)
- [Learning and Evaluating General Linguistic Intelligence](https://arxiv.org/abs/1901.11373v1)
- [The Value Function Polytope in Reinforcement Learning](https://arxiv.org/abs/1901.11524v1)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://arxiv.org/abs/1901.11504v1)
- [Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue](https://arxiv.org/abs/1901.11528v1)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://arxiv.org/abs/1902.00098v1)
- [Probabilistic Discriminative Learning with Layered Graphical Models](https://arxiv.org/abs/1902.00057v1)
- [Towards Machine-assisted Meta-Studies: The Hubble Constant](https://arxiv.org/abs/1902.00027v1)
- [DeepISP: Towards Learning an End-to-End Image Processing Pipeline](https://arxiv.org/abs/1801.06724v2)
- [Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going](https://arxiv.org/abs/1901.06955v2)
- [Combating Fake News: A Survey on Identification and Mitigation Techniques](https://arxiv.org/abs/1901.06437v1)
- [Go-Explore: a New Approach for Hard-Exploration Problems](https://arxiv.org/abs/1901.10995v1)
- [A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning](https://arxiv.org/abs/1901.10183v1)
- [Deep Learning for Video Game Playing](https://arxiv.org/abs/1708.07902v3)
- [Deep Discriminative Representation Learning with Attention Map for Scene Classification](https://arxiv.org/abs/1902.07967v1)
- [Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent](https://arxiv.org/abs/1902.06720v1)
- [Fixup Initialization: Residual Learning Without Normalization](https://arxiv.org/abs/1901.09321v1) :star:
- [Predictive Maintenance in Photovoltaic Plants with a Big Data Approach](https://arxiv.org/abs/1901.10855v1)

-------------

12/2018


- [Theory of Cognitive Relativity: A Promising Paradigm for True AI](https://arxiv.org/abs/1812.00136v1)
- [Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs](https://arxiv.org/abs/1812.01569v1)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://arxiv.org/abs/1812.01187v1)
- [Learning 3D Human Dynamics from Video](https://arxiv.org/abs/1812.01601v1)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://arxiv.org/abs/1812.01053v1)
- [Designing quantum experiments with a genetic algorithm](https://arxiv.org/abs/1812.01032v1)
- [SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications](https://arxiv.org/abs/1812.02132v1)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://arxiv.org/abs/1812.00332v1)
- [Brief survey of Mobility Analyses based on Mobile Phone Datasets](https://arxiv.org/abs/1812.01077v1)
- [Wireless Data Acquisition for Edge Learning: Importance Aware Retransmission](https://arxiv.org/abs/1812.02030v1)
- [An Introduction to Deep Reinforcement Learning](https://arxiv.org/abs/1811.12560v2)
- [Visual Object Networks: Image Generation with Disentangled 3D Representation](https://arxiv.org/abs/1812.02725v1)
- [Project Rosetta: A Childhood Social, Emotional, and Behavioral Developmental Ontology](https://arxiv.org/abs/1812.02722v1)
- [Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks](https://arxiv.org/abs/1811.03081v2)
- [Online Model Distillation for Efficient Video Inference](https://arxiv.org/abs/1812.02699v1)
- [Adversarial Vision Challenge](https://arxiv.org/abs/1808.01976v2)
- [Recursive Visual Attention in Visual Dialog](https://arxiv.org/abs/1812.02664v1)
- [Deep Architectures for Learning Context-dependent Ranking Functions](https://arxiv.org/abs/1803.05796v2)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://arxiv.org/abs/1805.10190v3)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://arxiv.org/abs/1812.01193v2)
- [Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs](https://arxiv.org/abs/1810.02419v2)
- [Tube-CNN: Modeling temporal evolution of appearance for object detection in video](https://arxiv.org/abs/1812.02619v1)
- [Computer Vision for Autonomous Vehicles](https://arxiv.org/abs/1812.02542v1)
- [A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization](https://arxiv.org/abs/1812.02500v1)
- [Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play](https://arxiv.org/abs/1802.06416v3)
- [Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting](https://arxiv.org/abs/1812.02464v1)
- [HAQ: Hardware-Aware Automated Quantization](https://arxiv.org/abs/1811.08886v2)
- [Meta-Transfer Learning for Few-Shot Learning](https://arxiv.org/abs/1812.02391v1)
- [Security and Privacy Issues in Deep Learning](https://arxiv.org/abs/1807.11655v2)
- [DNQ: Dynamic Network Quantization](https://arxiv.org/abs/1812.02375v1)
- [Summarizing Videos with Attention](https://arxiv.org/abs/1812.01969v1)
- [Exploring galaxy evolution with generative models](https://arxiv.org/abs/1812.01114v2)
- [EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search](https://arxiv.org/abs/1812.01190v2)
- [Visual Attention for Behavioral Cloning in Autonomous Driving](https://arxiv.org/abs/1812.01802v1)
- [Law and Adversarial Machine Learning](https://arxiv.org/abs/1810.10731v3)
- [A Style-Based Generator Architecture for Generative Adversarial Network](https://arxiv.org/abs/1812.04948v1)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://arxiv.org/abs/1812.05239v1)
- [Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation](https://arxiv.org/abs/1812.05477v1)
- [IRLAS: Inverse Reinforcement Learning for Architecture Search](https://arxiv.org/abs/1812.05285v1)
- [Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems](https://arxiv.org/abs/1812.05256v1)
- [Adversarial Transfer Learning](https://arxiv.org/abs/1812.02849v1)
- [Neural Approaches to Conversational AI](https://arxiv.org/abs/1809.08267v2)
- [Revisiting Exploration-Conscious Reinforcement Learning](https://arxiv.org/abs/1812.05551v1)
- [Deep Face Image Retrieval: a Comparative Study with Dictionary Learning](https://arxiv.org/abs/1812.05490v1)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://arxiv.org/abs/1812.05271v1)
- [A comparison of cluster algorithms as applied to unsupervised surveys](https://arxiv.org/abs/1811.12210v2)
- [On-line Adaptative Curriculum Learning for GANs](https://arxiv.org/abs/1808.00020v5)
- [Transfer Learning using Representation Learning in Massive Online Open Courses](https://arxiv.org/abs/1812.05043v1)
- [DeepProbLog: Neural Probabilistic Logic Programming](https://arxiv.org/abs/1805.10872v2)
- [Linking Artificial Intelligence Principles](https://arxiv.org/abs/1812.04814v1)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://arxiv.org/abs/1811.00145v2)
- [Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation](https://arxiv.org/abs/1812.05477v1)
- [Comixify: Transform video into a comics](https://arxiv.org/abs/1812.03473v1)
- [Policy Search in Continuous Action Domains: an Overview](https://arxiv.org/abs/1803.04706v4)
- [Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders](https://arxiv.org/abs/1812.06857v1)
- [Bayesian Optimization in AlphaGo](https://arxiv.org/abs/1812.06855v1)
- [A Tutorial on Deep Latent Variable Models of Natural Language](https://arxiv.org/abs/1812.06834v1)
- [Traceability of Deep Neural Networks](https://arxiv.org/abs/1812.06744v1)
- [TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph](https://arxiv.org/abs/1812.06722v1)
- [Towards Real-Time Accurate Object Detection in Both Images and Videos Based on Dual Refinement](https://arxiv.org/abs/1807.08638v3)
- [The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective](https://arxiv.org/abs/1812.05794v2)
- [Contingency-Aware Exploration in Reinforcement Learning](https://arxiv.org/abs/1811.01483v2)
- [Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference](https://arxiv.org/abs/1809.01357v2)
- [Latent Dirichlet Allocation in Generative Adversarial Networks](https://arxiv.org/abs/1812.06571v1)
- [Quantum Memristors in Quantum Photonics](https://arxiv.org/abs/1709.07808v2)
- [A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Software](https://arxiv.org/abs/1812.05944v1)
- [Evolutionary Neural Architecture Search for Image Restoration](https://arxiv.org/abs/1812.05866v1)
- [DVC: An End-to-end Deep Video Compression Framework](https://arxiv.org/abs/1812.00101v2)
- [Soft Actor-Critic Algorithms and Applications](https://arxiv.org/abs/1812.05905v1)
- [Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera](https://arxiv.org/abs/1812.05841v1)
- [End to End Video Segmentation for Driving : Lane Detection For Autonomous Car](https://arxiv.org/abs/1812.05914v1)
- [Deep Learning on Graphs: A Survey](https://arxiv.org/abs/1812.04202v1)
- [Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures](https://arxiv.org/abs/1808.08946v3)
- [Scaling shared model governance via model splitting](https://arxiv.org/abs/1812.05979v1)
- [FML: Face Model Learning from Videos](https://arxiv.org/abs/1812.07603v1)
- [Generative One-Shot Learning (GOL): A Semi-Parametric Approach to One-Shot Learning in Autonomous Vision](https://arxiv.org/abs/1812.07567v1)
- [Machine Learning in Cyber-Security - Problems, Challenges and Data Sets](https://arxiv.org/abs/1812.07858v1)
- [Deep Paper Gestalt](https://arxiv.org/abs/1812.08775v1)
- [Deep Learning for Classical Japanese Literature](https://arxiv.org/abs/1812.01718v1)
- [Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks](https://arxiv.org/abs/1812.07760v1)
- [Progressive Data Science: Potential and Challenges](https://arxiv.org/abs/1812.08032v1)
- [A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series](https://arxiv.org/abs/1812.07699v1)
- [Molecular Dynamics with Neural-Network Potentials](https://arxiv.org/abs/1812.07676v1)
- [Graph Neural Networks: A Review of Methods and Applications](https://arxiv.org/abs/1812.08434v1)
- [wav2letter++: The Fastest Open-source Speech Recognition System](https://arxiv.org/abs/1812.07625v1)
- [A Survey of Hierarchy Identification in Social Networks](https://arxiv.org/abs/1812.08425v1)
- [nocaps: novel object captioning at scale](https://arxiv.org/abs/1812.08658v1)
- [Optimization Methods for Large-Scale Machine Learning](https://arxiv.org/pdf/1606.04838v3.pdf)
- [Deep Graph Infomax](https://arxiv.org/abs/1809.10341v2)
- [Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems](https://arxiv.org/abs/1812.08879v1)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://arxiv.org/abs/1812.08989v1)
- [GamePad: A Learning Environment for Theorem Proving](https://arxiv.org/abs/1806.00608v2)
- [Lenia - Biology of Artificial Life](https://arxiv.org/abs/1812.05433v2)
- [Generalisation in humans and deep neural networks](https://arxiv.org/abs/1808.08750v2)
- [Learning from Web Data: the Benefit of Unsupervised Object Localization](https://arxiv.org/abs/1812.09232v1)
- [Learning to Navigate the Web](https://arxiv.org/abs/1812.09195v1)
- [Sample Efficient Adaptive Text-to-Speech](https://arxiv.org/abs/1809.10460v2)
- [A Gentle Introduction to Deep Learning in Medical Image Processing](https://arxiv.org/abs/1810.05401v2)
- [ProMP: Proximal Meta-Policy Search](https://arxiv.org/abs/1810.06784v3)
- [A Multi-task Neural Approach for Emotion Attribution, Classification and Summarization](https://arxiv.org/abs/1812.09041v1)
- [Face Hallucination Revisited: An Exploratory Study on Dataset Bias](https://arxiv.org/abs/1812.09010v1)
- [Lifelong Testing of Smart Autonomous Systems by Shepherding a Swarm of Watchdog Artificial Intelligence Agents](https://arxiv.org/abs/1812.08960v1)
- [Analysis Methods in Neural Language Processing: A Survey](https://arxiv.org/abs/1812.08951v1)
- [Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications](https://arxiv.org/abs/1805.07687v3)
- [Dialog-based Interactive Image Retrieval](https://arxiv.org/abs/1805.00145v3)
- [Quantifying Generalization in Reinforcement Learning](https://arxiv.org/abs/1812.02341v2)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://arxiv.org/abs/1812.08685v1)
- [Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code](https://arxiv.org/abs/1804.10694v5)
- [Generalization Error in Deep Learning](https://arxiv.org/abs/1808.01174v2)
- [Photo Wake-Up: 3D Character Animation from a Single Photo](https://arxiv.org/abs/1812.02246v1)
- [Data-Efficient Hierarchical Reinforcement Learning](https://arxiv.org/abs/1805.08296v4)
- [A General Approach to Domain Adaptation with Applications in Astronomy](https://arxiv.org/abs/1812.08839v1)
- [A Survey on Deep Learning for Named Entity Recognition](https://arxiv.org/abs/1812.09449v1)
- [Parallelized Interactive Machine Learning on Autonomous Vehicles](https://arxiv.org/abs/1812.09724v1)
- [Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness](https://arxiv.org/abs/1812.10404v1)
- [Training Deep Capsule Networks](https://arxiv.org/abs/1812.09707v1) :star:
- [Deep Learning for Inferring the Surface Solar Irradiance from Sky Imagery](https://arxiv.org/abs/1812.09793v1)
- [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://arxiv.org/abs/1812.10352v1)
- [RegNet: Learning the Optimization of Direct Image-to-Image Pose Registration](https://arxiv.org/abs/1812.10212v1)
- [GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language](https://arxiv.org/abs/1812.09064v1)
- [Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks](https://arxiv.org/abs/1812.09755v1)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://arxiv.org/abs/1812.10464v1)
- [Structure Learning of Sparse GGMs over Multiple Access Networks](https://arxiv.org/abs/1812.10437v1)
- [Large Multistream Data Analytics for Monitoring and Diagnostics in Manufacturing Systems](https://arxiv.org/abs/1812.10430v1)
- [ECG Segmentation by Neural Networks: Errors and Correction](https://arxiv.org/abs/1812.10386v1)
- [A Review on The Use of Deep Learning in Android Malware Detection](https://arxiv.org/abs/1812.10360v1)
- [Informative Object Annotations: Tell Me Something I Don't Know](https://arxiv.org/abs/1812.10358v1)
- [A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition](https://arxiv.org/abs/1812.10328v1)
- [Reward Constrained Policy Optimization](https://arxiv.org/abs/1805.11074v3)
- [Perceptrons from Memristors](https://arxiv.org/abs/1807.04912v2)
- [How Powerful are Graph Neural Networks?](https://arxiv.org/abs/1810.00826v2)
- [Optimizing Market Making using Multi-Agent Reinforcement Learning](https://arxiv.org/abs/1812.10252v1)
- [End-to-End Latent Fingerprint Search](https://arxiv.org/abs/1812.10213v1)
- [When Does Stochastic Gradient Algorithm Work Well?](https://arxiv.org/abs/1801.06159v2)
- [General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms](https://arxiv.org/abs/1802.10363v3)
- [Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning](https://arxiv.org/abs/1812.10183v1)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://arxiv.org/abs/1812.11794v1) :star:
- [Fingerprint Presentation Attack Detection: Generalization and Efficiency](https://arxiv.org/abs/1812.11574v1)
- [Tied Hidden Factors in Neural Networks for End-to-End Speaker Recognition](https://arxiv.org/abs/1812.11946v1)
- [SLIM LSTMs](https://arxiv.org/abs/1812.11391v1)
- [Partially Non-Recurrent Controllers for Memory-Augmented Neural Networks](https://arxiv.org/abs/1812.11485v1)
- [ATHENA: Automated Tuning of Genomic Error Correction Algorithms using Language Models](https://arxiv.org/abs/1812.11467v1)
- [Deep Residual Learning in the JPEG Transform Domain](https://arxiv.org/abs/1812.11690v1)
- [A dataset of 40K naturalistic 6-degree-of-freedom robotic grasp demonstrations](https://arxiv.org/abs/1812.11683v1)
- [ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers](https://arxiv.org/abs/1812.11677v1)
- [Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach](https://arxiv.org/abs/1812.11670v1)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://arxiv.org/abs/1812.05239v1)
- [Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies](https://arxiv.org/abs/1812.11314v1)
- [Greedy Layerwise Learning Can Scale to ImageNet](https://arxiv.org/abs/1812.11446v1)
- [CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras](https://arxiv.org/abs/1812.11209v1)
- [Learn to Interpret Atari Agents](https://arxiv.org/abs/1812.11276v1)
- [BNN+: Improved Binary Network Training](https://arxiv.org/abs/1812.11800v1)
- [AIR5: Five Pillars of Artificial Intelligence Research](https://arxiv.org/abs/1812.11509v1)
- [Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching](https://arxiv.org/abs/1812.11561v1)
- [CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences](https://arxiv.org/abs/1812.11501v1)
- [ORIGAMI: A Heterogeneous Split Architecture for In-Memory Acceleration of Learning](https://arxiv.org/abs/1812.11473v1)
- [Learning to Design RNA](https://arxiv.org/abs/1812.11951v1)
- [Safety and Trustworthiness of Deep Neural Networks: A Survey](https://arxiv.org/abs/1812.08342v2) :star:
- [Deep Learning on Graphs: A Survey](https://arxiv.org/abs/1812.04202v1)
- [Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks](https://arxiv.org/abs/1808.07647v2)
- [Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3](https://arxiv.org/abs/1812.10968v1)


-------------
11/2018

- [Horizon: Facebook's Open Source Applied Reinforcement Learning Platform](https://arxiv.org/pdf/1811.00260v1.pdf) [[code](https://github.com/facebookresearch/Horizon)]
- [Applications of Deep Learning to Nuclear Fusion Research](https://arxiv.org/pdf/1811.00333v1.pdf)
- [Towards Explainable NLP: A Generative Explanation Framework for Text Classification](https://arxiv.org/pdf/1811.00196v1.pdf)
- [CariGANs: Unpaired Photo-to-Caricature Translation](https://arxiv.org/pdf/1811.00222v2.pdf)
- [Predicting Demographics, Moral Foundations, and Human Values from Digital Behaviors](https://arxiv.org/abs/1712.01930v3)
- [Algorithms for screening of Cervical Cancer: A chronological review](https://arxiv.org/ftp/arxiv/papers/1811/1811.00849.pdf)
- [A Survey on Natural Language Processing for Fake News Detection](https://arxiv.org/pdf/1811.00770v1.pdf)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://arxiv.org/pdf/1811.00982v1.pdf)
- [A Fast Algorithm for Clustering of High
Dimensional Feature Vectors](https://arxiv.org/pdf/1811.00956v1.pdf)
- [Toward an AI Physicist for Unsupervised Learning](https://arxiv.org/pdf/1810.10525v2.pdf)
- [You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization](https://arxiv.org/pdf/1811.01567v1.pdf)
- [Evolutionary Data Measures: Understanding the Difficulty of Text
Classification Tasks](https://arxiv.org/pdf/1811.01910v1.pdf)
- [Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference](https://arxiv.org/abs/1811.01463v1)
- [Language GANs Falling Short](https://arxiv.org/pdf/1811.02549v2.pdf) 
- [Are Deep Policy Gradient Algorithms
Truly Policy Gradient Algorithms?](https://arxiv.org/pdf/1811.02553v1.pdf)
- [Learning from Demonstration in the Wild](https://arxiv.org/pdf/1811.03516v1.pdf)
- [A Survey on Data Collection for Machine Learning: a Big Data - AI Integration Perspective](https://arxiv.org/pdf/1811.03402v1.pdf)
- [Mesh-TensorFlow: Deep Learning for Supercomputers](https://arxiv.org/pdf/1811.02084v1.pdf)
- [ImageNet/ResNet-50 Training in 224 Seconds](https://arxiv.org/abs/1811.05233v1)
- [Natural Environment Benchmarks for Reinforcement Learning](https://arxiv.org/abs/1811.06032v1)
- [Reward learning from human preferences and demonstrations in Atari](https://arxiv.org/abs/1811.06521v1)  :star:
- [Mathematical Analysis of Adversarial Attacks](https://arxiv.org/abs/1811.06492v1)
- [Going Deeper with Semantics: Video Activity Interpretation using Semantic Contextualization](https://arxiv.org/abs/1708.03725v3)
- [Towards Explainable Deep Learning for Credit Lending: A Case Study](https://arxiv.org/abs/1811.06471v1)
- [Preliminary Studies on a Large Face Database](https://arxiv.org/abs/1811.06446v1)
- [Effect of data reduction on sequence-to-sequence neural TTS](https://arxiv.org/abs/1811.06315v1)
- [Effects of Dataset properties on the training of GANs](https://arxiv.org/abs/1811.02850v3)
- [On-line Adaptative Curriculum Learning for GANs](https://arxiv.org/abs/1808.00020v4)
- [GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism](https://arxiv.org/abs/1811.06965v1)
- [Active Inverse Reward Design](https://arxiv.org/abs/1809.03060v2)
- [DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks](https://arxiv.org/abs/1811.06783v1)
- [Incentivizing the Dynamic Workforce: Learning Contracts in the Gig-Economy](https://arxiv.org/abs/1811.06736v1)
- [ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion](https://arxiv.org/abs/1811.01609v2)
- [An Algorithmic Perspective on Imitation Learning](https://arxiv.org/abs/1811.06711v1)
- [DeRPN: Taking a further step toward more general object detection](https://arxiv.org/abs/1811.06700v1)
- [Activation Maximization Generative Adversarial Nets](https://arxiv.org/abs/1703.02000v9)
- [CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis](https://arxiv.org/abs/1809.05782v2)
- [Streaming End-to-end Speech Recognition For Mobile Devices](https://arxiv.org/abs/1811.06621v1)
- [Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps](https://arxiv.org/abs/1811.06606v1)
- [Scalable agent alignment via reward modeling: a research direction](https://arxiv.org/abs/1811.07871v1) :star:
- [Automatic Paper Summary Generation from Visual and Textual Information](https://arxiv.org/abs/1811.06943v1)
- [Learned Video Compression](https://arxiv.org/abs/1811.06981v1)
- [PydMobileNet: Improved Version of MobileNets with Pyramid Depthwise Separable Convolution](https://arxiv.org/abs/1811.07083v1)
- [Image Classification at Supercomputer Scale](https://arxiv.org/abs/1811.06992v1)
- [The PyTorch-Kaldi Speech Recognition Toolkit](https://arxiv.org/abs/1811.07453v1)
- [Adversarial Autoencoders for Generating 3D Point Clouds](https://arxiv.org/abs/1811.07605v1)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://arxiv.org/abs/1809.11096v1)
- [An Introduction to Image Synthesis with Generative Adversarial Nets](https://arxiv.org/abs/1803.04469v2)
- [When Conventional machine learning meets neuromorphic engineering: Deep Temporal Networks (DTNets) a machine learning frawmework allowing to operate on Events and Frames and implantable on Tensor Flow Like Hardware](https://arxiv.org/abs/1811.07672v1)
- [Improving Natural Language Inference Using External Knowledge in the Science Questions Domain](https://arxiv.org/abs/1809.05724v1)
- [On Human Robot Interaction using Multiple Modes](https://arxiv.org/abs/1811.07206v1)
- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882v1)
- [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://arxiv.org/abs/1811.07234v1)
- [Pitfalls of Graph Neural Network Evaluation](https://arxiv.org/abs/1811.05868v1)
- [Emergence of linguistic conventions in multi-agent reinforcement learning](https://arxiv.org/abs/1811.07208v1)
- [The Mirage of Action-Dependent Baselines in Reinforcement Learning](https://arxiv.org/abs/1802.10031v3)
- [Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning](https://arxiv.org/abs/1711.08921v2)
- [The Mafiascum Dataset: A Large Text Corpus for Deception Detection](https://arxiv.org/abs/1811.07851v1)
- [Deeper Interpretability of Deep Networks](https://arxiv.org/abs/1811.07807v1)
- [High Speed Event-based Face Detection and Tracking in the Blink of an Eye](https://arxiv.org/abs/1803.10106v2)
- [Explicit Bias Discovery in Visual Question Answering Models](https://arxiv.org/abs/1811.07789v1)
- [Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks](https://arxiv.org/abs/1810.11546v2)
- [Towards Global Explanations for Credit Risk Scoring](https://arxiv.org/abs/1811.07698v1)
- [Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks](https://arxiv.org/abs/1811.07640v1)
- [Deep Active Learning with a Neural Architecture Search](https://arxiv.org/abs/1811.07579v1)
- [iQIYI-VID: A Large Dataset for Multi-modal Person Identification](https://arxiv.org/abs/1811.07548v1)
- [HAQ: Hardware-Aware Automated Quantization](https://arxiv.org/abs/1811.08886v1)
- [Artificial Intelligence-Defined 5G Radio Access Networks](https://arxiv.org/abs/1811.08792v1)
- [Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs](https://arxiv.org/abs/1811.08634v1)
- [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://arxiv.org/abs/1811.10104v1)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://arxiv.org/abs/1606.08813v3)
- [Gradient Descent Finds Global Minima of Deep Neural Networks](https://arxiv.org/abs/1811.03804v1)
- [GAN Dissection: Visualizing and Understanding Generative Adversarial Networks](https://arxiv.org/abs/1811.10597v1)
- [Context-Aware Crowd Counting](https://arxiv.org/abs/1811.10452v1)
- [InstaNAS: Instance-aware Neural Architecture Search](https://arxiv.org/abs/1811.10201v1)
- [Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications](https://arxiv.org/abs/1811.09886v1)
- [Machine Learning for Health (ML4H) Workshop at NeurIPS 2018](https://arxiv.org/abs/1811.07216v2)
- [Representation Mixing for TTS Synthesis](https://arxiv.org/abs/1811.07240v2)
- [Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search](https://arxiv.org/abs/1811.06272v1)
- [Robust Artificial Intelligence and Robust Human Organizations](https://arxiv.org/abs/1811.10840v1) :star:
- [Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions](https://arxiv.org/abs/1811.10652v1)
- [MLModelScope: Evaluate and Measure ML Models within AI Pipelines](https://arxiv.org/abs/1811.09737v1)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://arxiv.org/abs/1811.10830v1)
- [Interpretable Convolutional Filters with SincNet](https://arxiv.org/abs/1811.09725v1)
- [Dataset Distillation](https://arxiv.org/abs/1811.10959v1)
- [LSTA: Long Short-Term Attention for Egocentric Action Recognition](https://arxiv.org/abs/1811.10698v1)
- [Feature-Fused SSD: Fast Detection for Small Objects](https://arxiv.org/abs/1709.05054v3)
- [Deformable ConvNets v2: More Deformable, Better Results](https://arxiv.org/abs/1811.11168v1)
- [TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer](https://arxiv.org/abs/1811.09620v1)
- [A computational perspective of the role of Thalamus in cognition](https://arxiv.org/abs/1803.00997v3)
- [Towards Long-Term Memory for Social Robots: Proposing a New Challenge for the RoboCup@Home League](https://arxiv.org/abs/1811.10758v1)
- [Automated Algorithm Selection: Survey and Perspectives](https://arxiv.org/abs/1811.11597v1)
- [LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild](https://arxiv.org/abs/1810.06990v4)
- [Towards Decentralization of Social Media](https://arxiv.org/abs/1811.11522v1)
- [Neuro-memristive Circuits for Edge Computing: A review](https://arxiv.org/abs/1807.00962v2)
- [Deep Reinforcement Learning for Autonomous Driving](https://arxiv.org/abs/1811.11329v1)
- [Actor-Critic Policy Optimization in Partially Observable Multiagent Environments](https://arxiv.org/abs/1810.09026v3)
- [Prioritizing Starting States for Reinforcement Learning](https://arxiv.org/abs/1811.11298v1)
- [DLHub: Model and Data Serving for Science](https://arxiv.org/abs/1811.11213v1)
- [Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview](https://arxiv.org/abs/1811.11669v1)
- [Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets](https://arxiv.org/abs/1811.11618v1)
- [Snap ML: A Hierarchical Framework for Machine Learning](https://arxiv.org/abs/1803.06333v3)
- [Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach](https://arxiv.org/abs/1811.12296v1)
- [Demonstrating Advantages of Neuromorphic Computation: A Pilot Study](https://arxiv.org/abs/1811.03618v3)
- [ShelfNet for Real-time Semantic Segmentation](https://arxiv.org/abs/1811.11254v1)
- [Attentive Relational Networks for Mapping Images to Scene Graphs](https://arxiv.org/abs/1811.10696v1)
- [ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving](https://arxiv.org/abs/1811.12222v1)
- [Recurrent Relational Networks](https://arxiv.org/abs/1711.08028v4)
- [Fashion-Gen: The Generative Fashion Dataset and Challenge](https://arxiv.org/abs/1806.08317v2)
- [A Century Long Commitment to Assessing Artificial Intelligence and its Impact on Society](https://arxiv.org/abs/1808.07899v1)
- [Foot Pressure from Video: A Deep Learning Approach to Predict Dynamics from Kinematics](https://arxiv.org/abs/1811.12607v1)
- [Measure, Manifold, Learning, and Optimization: A Theory Of Neural Networks](https://arxiv.org/abs/1811.12783v1)
- [Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects](https://arxiv.org/abs/1806.01794v2)


------------

10/2018

- [The Profiling Machine: Active Generalization over Knowledge](https://arxiv.org/pdf/1810.00782v1.pdf)
- [AI Benchmark: Running Deep Neural Networks
on Android Smartphones](https://arxiv.org/pdf/1810.01109v1.pdf)
- [A deep learning pipeline for product recognition on store shelves](https://arxiv.org/pdf/1810.01733v2.pdf)
- [MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning](https://arxiv.org/ftp/arxiv/papers/1810/1810.02272.pdf)
- [Quantum Artificial Life in an IBM Quantum Computer](https://arxiv.org/pdf/1711.09442v2.pdf)
- [Finding Solutions to Generative Adversarial Privacy](https://arxiv.org/pdf/1810.02069v1.pdf)
- [Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA](https://arxiv.org/pdf/1810.02068v1.pdf)
- [Scaling Video Analytics Systems to Large Camera Deployments](https://arxiv.org/pdf/1809.02318v2.pdf)
- [From Soft Classifiers to Hard Decisions: How fair can we be?](https://arxiv.org/pdf/1810.02003v1.pdf)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://arxiv.org/pdf/1810.01943v1.pdf)
- [Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem](https://arxiv.org/ftp/arxiv/papers/1810/1810.01940.pdf)
- [Auto-Keras: Efficient Neural Architecture Search with Network Morphism](https://arxiv.org/pdf/1806.10282v2.pdf)
- [An Overview on Application of Machine Learning
Techniques in Optical Networks](https://arxiv.org/pdf/1803.07976v3.pdf)
- [A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models](https://arxiv.org/pdf/1810.04261v1.pdf)
- [Transfer Metric Learning: Algorithms, Applications and Outlooks](https://arxiv.org/pdf/1810.03944v2.pdf)
- [Secure Deep Learning Engineering: A Software Quality Assurance Perspective](https://arxiv.org/pdf/1810.04538v1.pdf)
- [BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding](https://arxiv.org/pdf/1810.04805v1.pdf)  [[code](https://github.com/codertimo/BERT-pytorch)] [[code](https://github.com/google-research/bert)]  :star:
- [Is multiagent deep reinforcement learning the answer or the question? A brief survey](https://arxiv.org/pdf/1810.05587v1.pdf)
- [CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning](https://arxiv.org/pdf/1810.06284v1.pdf)
- [On the Margin Theory of Feedforward Neural Networks](https://arxiv.org/pdf/1810.05369v1.pdf)
- [Private Machine Learning in TensorFlow
using Secure Computation](https://arxiv.org/pdf/1810.08130v1.pdf)
- [Applications of Deep Reinforcement Learning in
Communications and Networking: A Survey](https://arxiv.org/pdf/1810.07862v1.pdf)
- [Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs](https://arxiv.org/pdf/1810.09868v1.pdf)
- [Intrinsic Social Motivation via Causal Influence in Multi-Agent RL](https://arxiv.org/pdf/1810.08647v1.pdf)
- [Do Deep Generative Models Know What They Don't Know?](https://arxiv.org/pdf/1810.09136v1.pdf)
- [Neighbourhood Consensus Networks](https://arxiv.org/pdf/1810.10510v1.pdf)
- [The Uncertainty Bellman Equation and Exploration](https://arxiv.org/pdf/1709.05380.pdf)
- [Humans are still the best lossy image compressors](https://arxiv.org/pdf/1810.11137v2.pdf)
- [Model-Based Active Exploration](https://arxiv.org/pdf/1810.12162v1.pdf)
- [Reversible Recurrent Neural Networks](https://arxiv.org/pdf/1810.10999v1.pdf)
- [Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation](https://arxiv.org/pdf/1810.09202v1.pdf)
- [Multi-Agent Common Knowledge Reinforcement Learning](https://arxiv.org/pdf/1810.11702v1.pdf)
- [Tackling Sequence to Sequence Mapping Problems with Neural Networks](https://arxiv.org/pdf/1810.10802v1.pdf)
- [Applying Deep Learning To Airbnb Search](https://arxiv.org/abs/1810.09591v2)
- [Inverse reinforcement learning for video games](https://arxiv.org/pdf/1810.10593v1.pdf)
- [Fast and accurate object detection in high resolution 4K and 8K video using GPUs](https://arxiv.org/pdf/1810.10551v1.pdf)
- [Towards Efficient Large-Scale Graph Neural Network Computing](https://arxiv.org/pdf/1810.08403v1.pdf)
- [An Exploration of Dropout with RNNs for Natural Language Inference](https://arxiv.org/pdf/1810.08606v1.pdf)
- [Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells](https://arxiv.org/pdf/1810.10804v1.pdf)
- [Memory Replay GANs: learning to generate images from new categories without forgetting](https://arxiv.org/pdf/1809.02058v2.pdf)
- [You May Not Need Attention](https://arxiv.org/pdf/1810.13409.pdf) [[code](https://github.com/ofirpress/YouMayNotNeedAttention)]
- [Face Recognition: From Traditional to Deep Learning Methods](https://arxiv.org/pdf/1811.00116v1.pdf)
- [Democratizing Production-Scale Distributed Deep Learning](https://arxiv.org/pdf/1811.00143v1.pdf)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://arxiv.org/pdf/1811.00145v1.pdf)
- [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://arxiv.org/pdf/1811.00002v1.pdf)
- [Dial2Desc: End-to-end Dialogue Description Generation](https://arxiv.org/pdf/1811.00185v1.pdf)
- [Taking Human out of Learning Applications: A Survey on Automated Machine Learning](https://arxiv.org/pdf/1810.13306v1.pdf)
- [DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution](https://arxiv.org/abs/1705.07386v4)


-----------

09/2018

- [Hamiltonian Descent Methods](https://arxiv.org/pdf/1809.05042v1.pdf)
- [Deep Learning for Generic Object Detection: A Survey](https://arxiv.org/pdf/1809.02165v1.pdf)
- [Unity: A General Platform for Intelligent Agents](https://arxiv.org/pdf/1809.02627v1.pdf)
- [Five lessons from building a deep neural network recommender
for marketplaces](https://arxiv.org/pdf/1809.02131v1.pdf)
- [Focus Group on Artificial Intelligence for Health](https://arxiv.org/ftp/arxiv/papers/1809/1809.04797.pdf)
- [Deep Learning in Information Security](https://arxiv.org/pdf/1809.04332v1.pdf)
- [LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts](https://arxiv.org/pdf/1809.04938v1.pdf)
- [IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles](https://arxiv.org/pdf/1809.05054v1.pdf)
- [Identifying Real Estate Opportunities
using Machine Learning](https://arxiv.org/pdf/1809.04933v1.pdf)
- [Deep Learning Towards Mobile Applications](https://arxiv.org/pdf/1809.03559v1.pdf)
- [HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments](https://arxiv.org/pdf/1801.07746v2.pdf)
- [Urban morphology meets deep learning: Exploring
urban forms in one million cities, town and villages
across the planet](https://arxiv.org/pdf/1709.02939v2.pdf)
- [Graph Convolutional Networks for Text Classification](https://arxiv.org/pdf/1809.05679v1.pdf)
- [Identifying Generalization Properties in Neural Networks](https://arxiv.org/pdf/1809.07402v1.pdf)
- [Towards Deep and Representation Learning for Talent Search at LinkedIn](https://arxiv.org/pdf/1809.06473v1.pdf)
- [Cellular automata as convolutional neural networks](https://arxiv.org/pdf/1809.02942v1.pdf)
- [Realistic Evaluation of Deep Semi-Supervised Learning Algorithms](https://arxiv.org/pdf/1804.09170v2.pdf)
- [AlphaSeq: Sequence Discovery with Deep Reinforcement Learning](https://arxiv.org/pdf/1810.01218v1.pdf)
- [Learning to Interpret Satellite Images Using Wikipedia](https://arxiv.org/pdf/1809.10236v1.pdf)
- [Visual Analytics for Automated Model Discovery](https://arxiv.org/pdf/1809.10782v2.pdf)
- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://arxiv.org/pdf/1809.11165v2.pdf)
- [Stakeholders in Explainable AI](https://arxiv.org/pdf/1810.00184v1.pdf)
- [Adversarial Attacks and Defences: A Survey](https://arxiv.org/pdf/1810.00069v1.pdf)
- [Audio-Visual Speech Recognition With A Hybrid CTC/Attention Architecture](https://arxiv.org/pdf/1810.00108v1.pdf)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://arxiv.org/pdf/1803.04311v2.pdf)
- [Hierarchical Deep Multiagent Reinforcement Learning](https://arxiv.org/pdf/1809.09332v1.pdf)
- [Massively Parallel Hyperparameter Tuning](https://arxiv.org/abs/1810.05934v3)

-------------

08/2018

- [Backprop Evolution](https://arxiv.org/pdf/1808.02822v1.pdf)
- [Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation](https://arxiv.org/abs/1808.00769v1)
- Large Scale Language Modeling: Converging on 40GB of Text in Four Hours. [paper](https://arxiv.org/pdf/1808.01371v1.pdf)
- [RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising](https://arxiv.org/pdf/1808.00720v1.pdf)
- [Neural Arithmetic Logic Units](https://arxiv.org/pdf/1808.00508v1.pdf)
- [CornerNet: Detecting Objects as Paired Keypoints](https://arxiv.org/abs/1808.01244v1)
- [A Survey on Deep Transfer Learning](https://arxiv.org/pdf/1808.01974v1.pdf)
- [YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud](https://arxiv.org/pdf/1808.02350v1.pdf)
- [Dialog-context aware end-to-end speech recognition](https://arxiv.org/pdf/1808.02171v1.pdf)
- [An Efficient Deep Reinforcement Learning Model for Urban Traffic Control](https://arxiv.org/pdf/1808.01876v1.pdf)
- [Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis](https://arxiv.org/pdf/1808.01410v1.pdf)
- [GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware](https://arxiv.org/pdf/1808.01363v1.pdf)
- [Application of End-to-End Deep Learning in Wireless Communications Systems](https://arxiv.org/pdf/1808.02394v1.pdf)
- [A Short Note about Kinetics-600](https://arxiv.org/pdf/1808.01340v1.pdf)
- [Smart City Development with Urban Transfer Learning](https://arxiv.org/abs/1808.01552v1)
- [Deep Learning Advances on Different 3D Data Representations: A Survey](https://arxiv.org/pdf/1808.01462v1.pdf)
- Skill Rating for Generative Models. [paper](https://arxiv.org/pdf/1808.04888v1.pdf)
- Fundamentals of Recurrent Neural Network (RNN)
and Long Short-Term Memory (LSTM) Network. [paper](https://arxiv.org/pdf/1808.03314v1.pdf)
- Hierarchical Attention: What Really Counts in
Various NLP Tasks
. [paper](https://arxiv.org/pdf/1808.03728v1.pdf)
- Neural Architecture Search: A Survey. [paper](https://arxiv.org/pdf/1808.05377v1.pdf)
- Rapid Adaptation of Neural Machine Translation to New Languages. [paper](https://arxiv.org/pdf/1808.04189v1.pdf)
- A Survey on Sentiment and Emotion Analysis for
Computational Literary Studies. [paper](https://arxiv.org/pdf/1808.03137v1.pdf)
- Video-to-Video Synthesis. [arxiv](https://arxiv.org/abs/1808.06601v1)  [[code](https://github.com/NVIDIA/vid2vid)]
- CoQA: A Conversational Question Answering Challenge. [arxiv](https://arxiv.org/abs/1808.07042v1)
- QuAC : Question Answering in Context. [arxiv](https://arxiv.org/pdf/1808.07036v1.pdf)
- Everybody Dance Now. [paper](https://arxiv.org/pdf/1808.07371v1.pdf)
- Contextual Parameter Generation for Universal Neural Machine Translation. [paper](https://arxiv.org/pdf/1808.08493v1.pdf)
- Fusion++: Volumetric Object-Level SLAM. [paper](https://arxiv.org/pdf/1808.08378v2.pdf)
- [Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs](https://arxiv.org/pdf/1808.07724v1.pdf)
- [FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations](https://arxiv.org/pdf/1808.09945v1.pdf)
- [Generalisation in humans and deep neural networks](https://arxiv.org/pdf/1808.08750v1.pdf)
- [All You Need is "Love": Evading Hate-speech Detection](https://arxiv.org/pdf/1808.09115v2.pdf)


------------------


07/2018

- [Troubling Trends in Machine Learning Scholarship](https://arxiv.org/pdf/1807.03341v1.pdf)
- [Machine Learning in High Energy Physics Community White Paper](https://arxiv.org/pdf/1807.02876v1.pdf)
- [Quasi-Dilemmas for Artificial Moral Agents](https://arxiv.org/pdf/1807.02572v1.pdf)
- [Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences](https://arxiv.org/abs/1807.02582v1)
- [Comparing Dynamics: Deep Neural Networks versus Glassy Systems](https://arxiv.org/abs/1803.06969v2)
- [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://arxiv.org/abs/1807.02787v1)
- [Natural Language Processing for Information Extraction](https://arxiv.org/abs/1807.02383v1)
- [Video Summarisation by Classification with Deep Reinforcement Learning](https://arxiv.org/abs/1807.03089v2)
- [Generative models on accelerated neuromorphic hardware](https://arxiv.org/pdf/1807.02389v1.pdf)
- [A Survey of Knowledge Representation and Retrieval for Learning in Service Robotics](https://arxiv.org/pdf/1807.02192v1.pdf)
- [Zipf's law in 50 languages: its structural pattern, linguistic interpretation, and cognitive motivation](https://arxiv.org/abs/1807.01855v1)
- [JUMPER: Learning When to Make Classification Decisions in Reading](https://arxiv.org/pdf/1807.02314v1.pdf)
- [Universal Word Segmentation: Implementation and Interpretation](https://arxiv.org/abs/1807.02974v1)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://arxiv.org/abs/1807.02371v1)
- [Taskonomy: Disentangling Task Transfer Learning](https://arxiv.org/pdf/1804.08328.pdf)
- ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech. [arxiv](https://arxiv.org/abs/1807.07281v1)
- Hierarchical Multi Task Learning With CTC. [arxiv](https://arxiv.org/abs/1807.07104v1)
- When deep learning meets security. [arxiv](https://arxiv.org/abs/1807.04739v1)
- Object Detection with Deep Learning: A Review. [arxiv](https://arxiv.org/abs/1807.05511v1)
- Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search. [arxiv](https://arxiv.org/abs/1807.06906v1)
- [Implementing Neural Turing Machines](https://arxiv.org/abs/1807.08518v1)
- [Talk the Walk: Navigating New York City through Grounded Dialogue](https://arxiv.org/abs/1807.03367)
- [The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks](https://arxiv.org/abs/1803.03635)
- [Learning by Asking Questions](https://arxiv.org/abs/1712.01238v1)
- [Deep learning in agriculture: A survey](https://arxiv.org/ftp/arxiv/papers/1807/1807.11809.pdf)
- [Learning Plannable Representations with Causal InfoGAN](https://arxiv.org/pdf/1807.09341v1.pdf), ICML / IJCAI / AAMAS 2018 Workshop on(PAL-18).
- [Progressive Neural Architecture Search](https://arxiv.org/abs/1712.00559v3)
- [Variational Bayesian Reinforcement Learning with Regret Bounds](https://arxiv.org/abs/1807.09647v1)
- [Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes](https://arxiv.org/abs/1807.11205v1)
- [Learning from Delayed Outcomes with Intermediate Observations](https://arxiv.org/abs/1807.09387v1)
- [A Survey of the Usages of Deep Learning in Natural Language Processing](https://arxiv.org/abs/1807.10854v1)
- [The GAN Landscape: Losses, Architectures, Regularization, and Normalization](https://arxiv.org/abs/1807.04720v1)
- [Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures](https://arxiv.org/abs/1807.04587v1)
- [TherML: Thermodynamics of Machine Learning](https://arxiv.org/abs/1807.04162v2)
- [Neural Processes](https://arxiv.org/abs/1807.01622v1)
- [GAN Q-learning](https://arxiv.org/abs/1805.04874v3)
- [LaneNet: Real-Time Lane Detection Networks for Autonomous Driving](https://arxiv.org/abs/1807.01726v1)
- [TextWorld: A Learning Environment for Text-based Games](https://arxiv.org/abs/1806.11532v1), ICML/IJCAI 2018.
- [Perceptrons from Memristors](https://arxiv.org/abs/1807.04912v1)
- [Efficient Probabilistic Inference in the Quest for
Physics Beyond the Standard Model
](https://arxiv.org/pdf/1807.07706v1.pdf)
- [Large-Scale Visual Speech Recognition](https://arxiv.org/abs/1807.05162)



********************************

----------------------

|| [dl-research-groups](https://github.com/gopala-kr/summary/tree/master/dl-research-groups) | [dl-frameworks](https://github.com/gopala-kr/weekend-with-github/tree/master/Projects-Blogs/06-ml-dl-frameworks) | [dl1](https://github.com/ty4z2008/Qix/blob/master/dl.md)+[dl2](https://github.com/ty4z2008/Qix/blob/master/dl2.md) | [awesome-dl](https://github.com/terryum/awesome-deep-learning-papers)+[roadmap](https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap)+[ml](https://github.com/josephmisiti/awesome-machine-learning) | [CB-Insights_AI-100](https://s3-us-west-2.amazonaws.com/cbi-content/research-reports/CB-Insights_AI-100-2017.pdf) + [AI100_2](https://www.cbinsights.com/research/artificial-intelligence-top-startups/) | [ai-angelList](https://angel.co/artificial-intelligence) | [machine-learning-surveys](https://github.com/mlreview/machine-learning-surveys) |[graph-based-deep-learning-literature](https://github.com/naganandy/graph-based-deep-learning-literature)||

|| [a--rnn](https://github.com/kjw0612/awesome-rnn) | [a-deep-vision](https://github.com/kjw0612/awesome-deep-vision) | [a--random-forest](https://github.com/kjw0612/awesome-random-forest) | [a-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision) | [a-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning) | [a-p](https://github.com/abhshkdz/papers) | [d-l-p](https://github.com/sbrugman/deep-learning-papers) | [drlp](https://github.com/junhyukoh/deep-reinforcement-learning-papers) | [DeepLearningImplementations](https://github.com/tdeboissiere/DeepLearningImplementations) | [drlp2](https://github.com/muupan/deep-reinforcement-learning-papers) | [dl-papernotes](https://github.com/dennybritz/deeplearning-papernotes) | [dl-r](https://github.com/endymecy/awesome-deeplearning-resources) | [med-appl](https://github.com/albarqouni/Deep-Learning-for-Medical-Applications) | [very-dl](https://github.com/daviddao/awesome-very-deep-learning) | [nn.papers](https://github.com/robertsdionne/neural-network-papers) | [a-GAN](https://github.com/nightrome/really-awesome-gan) | [rl.papers](https://github.com/junhyukoh/deep-reinforcement-learning-papers)| [dnn.med](https://github.com/TarrySingh/Deep-Neural-Networks-HealthCare) | [cuda.programming](https://github.com/Erkaman/Awesome-CUDA) | [caltech.cuda](http://courses.cms.caltech.edu/cs179/) | [rtb-papers](https://github.com/wnzhang/rtb-papers) | [deep-learning-nlp-rl-papers](https://github.com/madrugado/deep-learning-nlp-rl-papers) | [nips2017](https://github.com/hindupuravinash/nips2017) | [deep_learning_object_detection](https://github.com/hoya012/deep_learning_object_detection) | [MLiterature](https://github.com/jidroid404/MLiterature) || 



|| [Outline of artificial intelligence](https://en.wikipedia.org/wiki/Outline_of_artificial_intelligence) | [Outline of machine learning](https://en.wikipedia.org/wiki/Outline_of_machine_learning#Machine_learning_algorithms) | [Outline of computer vision](https://en.wikipedia.org/wiki/Outline_of_computer_vision) | [Outline of natural language processing](https://en.wikipedia.org/wiki/Outline_of_natural_language_processing) | [Outline of robotics](https://en.wikipedia.org/wiki/Outline_of_robotics) | [List of datasets for machine learning research](https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research)| [ai applications](https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence) | [UCI.ML.DATASETS](http://archive.ics.uci.edu/ml/index.php) | [public.datasets](https://github.com/caesar0301/awesome-public-datasets) | [kaggle.datasets](https://www.kaggle.com/datasets) | [datasets.gh.list](https://github.com/ChristosChristofidis/awesome-deep-learning#datasets) | [Origin_of_speech](https://en.wikipedia.org/wiki/Origin_of_speech) || 

|| [awesome-nlp](https://github.com/keon/awesome-nlp) | [nlp_tasks](https://github.com/Kyubyong/nlp_tasks) | [DeepNLP-models-Pytorch](https://github.com/DSKSD/DeepNLP-models-Pytorch) | [oxford.nlp.lectures](https://github.com/oxford-cs-deepnlp-2017/lectures) | [stanford.nlp.lectures](https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | [nltk.org/book](http://www.nltk.org/book/) | [DL4NLP](https://github.com/andrewt3000/DL4NLP) | [cs388.utexas.nlp](https://www.cs.utexas.edu/~mooney/cs388/) | [nlp-datasets](https://github.com/karthikncode/nlp-datasets) | [DL-NLP-Readings](https://github.com/IsaacChanghau/DL-NLP-Readings) | [gt-nlp-class](https://github.com/jacobeisenstein/gt-nlp-class)| [GNNPapers](https://github.com/thunlp/GNNPapers) ||


> **[[Deep Learning Resources](http://www.jeremydjacksonphd.com/category/deep-learning/)]**,  [[DL_libraries_final_Rankings](https://github.com/thedataincubator/data-science-blogs/blob/master/output/DL_libraries_final_Rankings.csv)]



------------


**ML/DL Publications**

- [Arxiv Sanity Preserver](http://www.arxiv-sanity.com/) 
- [NIPS](https://papers.nips.cc/) 
- [ICML-17](https://icml.cc/Conferences/2017/Schedule?type=Poster) 
- [AAAI](https://aaai.org/Publications/publications.php) 
- [CVPapers](http://www.cvpapers.com/index.html) 
- [arxiv.ml](https://arxiv.org/list/stat.ML/recent) 
- [arxiv.cvpr](https://arxiv.org/list/cs.CV/recent) 
- [arxiv.ai](https://arxiv.org/list/cs.AI/recent) 
- [arxiv.ne](https://arxiv.org/list/cs.NE/recent) 
- [arxiv.ro](https://arxiv.org/list/cs.RO/recent) 
- [arxiv.nc](https://arxiv.org/list/q-bio.NC/recent) 
- [hinton](http://www.cs.toronto.edu/~hinton/)
- [MILA](https://mila.quebec/en/publications/) 
- [FAIR](https://research.fb.com/publications/?cat=13) 
- [deepmind](https://deepmind.com/research/publications/) 
- [brain team](https://research.google.com/pubs/BrainTeam.html) 
- [andrewng](http://www.robotics.stanford.edu/~ang/papers.php) 
- [yann.lecun,nyu](http://yann.lecun.com/ex/index.html)  
- [manning](https://nlp.stanford.edu/manning/)  
- [hiit.neuro](http://www.hiit.fi/neuro)  
- [grey.colorado.edu.CCNL](https://grey.colorado.edu/CompCogNeuro/index.php/CCNLab/publications) 
- [dbsystem](http://www.comp.nus.edu.sg/~dbsystem/paper.html) 
- [nvidia.ml.research](http://research.nvidia.com/research-area/machine-learning-artificial-intelligence) 
- [stanford.vision.lab](http://vision.stanford.edu/publications.html) 
- [BAIR.people](http://bair.berkeley.edu/faculty.html) + [courses](http://bair.berkeley.edu/courses.html) 
- [CSAIL.people](https://www.csail.mit.edu/people?person%5B0%5D=role%3A299) + [publications](http://publications.csail.mit.edu/)
- [SAIL.people](http://ai.stanford.edu/#faculty) + [courses](http://ai.stanford.edu/courses/) 
- [google.machineIntelligence](https://research.google.com/pubs/MachineIntelligence.html) 
- [google.MachinePerception](https://research.google.com/pubs/MachinePerception.html) 
- [google.MachineTranslation](https://research.google.com/pubs/MachineTranslation.html) 
- [google.NaturalLanguageProcessing](https://research.google.com/pubs/NaturalLanguageProcessing.html) 
- [google.AIResidency](https://research.google.com/pubs/AIResidency.html) 
- [scholar.cvpr](https://scholar.google.es/citations?view_op=top_venues&hl=en&vq=eng_computervisionpatternrecognition) 
- [scholar.ai](https://scholar.google.es/citations?view_op=top_venues&hl=en&vq=eng_artificialintelligence) 
- [scholar.gt](https://scholar.google.es/citations?view_op=top_venues&hl=en&vq=eng_gametheorydecisionscience) 
- [scholar.ro](https://scholar.google.es/citations?view_op=top_venues&hl=en&vq=eng_robotics) 
- [shoham](http://robotics.stanford.edu/~shoham/YoavPublications.htm) 
- [ai.people](https://en.wikipedia.org/wiki/Category:Artificial_intelligence_researchers) 
- [ml.people](https://en.wikipedia.org/wiki/Category:Machine_learning_researchers) 
- [AAAI.fellows](https://en.wikipedia.org/wiki/Category:Fellows_of_the_Association_for_the_Advancement_of_Artificial_Intelligence) 
- [UTA.ai](http://www.cs.utexas.edu/users/ai-lab/pub-list.php#pubs) 
- [UTA.dml](http://www.cs.utexas.edu/users/dml/) 
- [wildml](http://www.wildml.com/) 
- [rl.gh.p](https://github.com/junhyukoh/deep-reinforcement-learning-papers) 
- [nn.gh.p](https://github.com/robertsdionne/neural-network-papers) 
- [openai.requests.research](https://github.com/openai/requests-for-research) 
-  [psy.ox.ac.uk](https://www.psy.ox.ac.uk/research/publications) 
- [cs.ox.ac.uk](https://www.cs.ox.ac.uk/activities/publications/date/machinelearning.html) 
- [CV.people](https://github.com/jbhuang0604/awesome-computer-vision/blob/master/people.md)  
- [arXivTimes](https://github.com/arXivTimes/arXivTimes)

--------------

**ML/DL Implementations**


- [deepmind.gh.code](https://github.com/deepmind) 
- [openai.gh.code](https://github.com/openai) 
- [facebookresearch.gh.code](https://github.com/facebookresearch) 
- [frameworks.dl](https://github.com/zer0n/deepframeworks)
- [ml.python](https://github.com/rasbt/python-machine-learning-book) 
-  [deep.nlp.models.pytorch](https://github.com/DSKSD/DeepNLP-models-Pytorch) 
-  [rl](https://github.com/yandexdataschool/Practical_RL) 
-  [dl.keras.tf](https://github.com/leriomaggio/deep-learning-keras-tensorflow) 
-  [pytorch.list](https://github.com/ritchieng/the-incredible-pytorch) 
-  [ml.algorithms](https://github.com/rushter/MLAlgorithms) 
-  [cnn.benchmarks](https://github.com/soumith/convnet-benchmarks) 
-  [pytorch.tut](https://github.com/pytorch/tutorials) 
-  [pytorch.ex](https://github.com/pytorch/examples) 
-  [torch.start](https://github.com/cvondrick/torch-starter) 
-  [dl.tool](https://github.com/rasmusbergpalm/DeepLearnToolbox) 
-  [rl.tf](https://github.com/carpedm20/deep-rl-tensorflow) 
-  [DCGAN.tf](https://github.com/carpedm20/DCGAN-tensorflow) 
-  [ntm.tf](https://github.com/carpedm20/NTM-tensorflow) 
-  [neural.style](https://github.com/jcjohnson/neural-style) 
-  [torch.rnn](https://github.com/jcjohnson/torch-rnn) 
-  [stanford.dl.ex](https://github.com/amaas/stanford_dl_ex) 
-  [deepq.tf](https://github.com/siemanko/tensorflow-deepq) 
-  [gan](https://github.com/goodfeli/adversarial) 
-  [ml.hackers.code.r](https://github.com/johnmyleswhite/ML_for_Hackers) 
-  [bayesian.code.jn](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) 
-  [nn.dl.code](https://github.com/mnielsen/neural-networks-and-deep-learning) 
-  [nn.java](https://github.com/ivan-vasilev/neuralnetworks) 
-  [ml.go](https://github.com/sjwhitworth/golearn) 
-  [numenta.gh.code](https://github.com/numenta) 
-  [tensorflow.gh.code](https://github.com/tensorflow) 
-  [neural.doodle.code](https://github.com/alexjc/neural-doodle) 
- [ml.sf.engineering](https://github.com/ZuzooVn/machine-learning-for-software-engineers) 
- [tf.models](https://github.com/tensorflow/models) 
- [DL4NLP](https://github.com/andrewt3000/DL4NLP) 
- [neural.style.torch](https://github.com/jcjohnson/neural-style) 
- [opencog](https://github.com/opencog/opencog) 
- [ml.jn.scikit](https://github.com/hangtwenty/dive-into-machine-learning) 
- [bayesian.PyMC3](https://github.com/markdregan/Bayesian-Modelling-in-Python) 
- [tf.ex](https://github.com/aymericdamien/TensorFlow-Examples) 
- [ALE](https://github.com/mgbellemare/Arcade-Learning-Environment) 
- [aima.py](https://github.com/aimacode/aima-python) 
- [deeplearning4j.code](https://github.com/deeplearning4j/deeplearning4j) 
- [tflearn.tf](https://github.com/tflearn/tflearn) 
- [resnet](https://github.com/KaimingHe/deep-residual-networks) 
- [stanford.tf](https://github.com/chiphuyen/stanford-tensorflow-tutorials) 
- [top.dl](https://github.com/aymericdamien/TopDeepLearning)
- [tensorlayer.dl.rl](https://github.com/zsdonghao/tensorlayer) 
- [dl.keras](https://github.com/fchollet/deep-learning-models) 
- [dl.docker](https://github.com/floydhub/dl-docker) 
- [dl.list](https://github.com/SamDeepLearning/The-Terrible-Deep-Learning-List) 
- [mila.gh.code](https://github.com/mila-udem) 
- [microsoft.gh.code](https://github.com/Microsoft) 
- [CSAILVision.gh.code](https://github.com/CSAILVision) 
- [NVIDIA.gh.code](https://github.com/NVIDIA) 
- [OpenNMT.code](https://github.com/OpenNMT) 
- [Tencent.code](https://github.com/Tencent) 
- [baidu.code](https://github.com/baidu) 
- [stanfordnlp.code](https://github.com/stanfordnlp) 
- [sar.ml](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems) 
- [sony.code](https://github.com/sony) 
- [ml](https://github.com/eriklindernoren/ML-From-Scratch) 
- [PrincetonVision.code](https://github.com/PrincetonVision) 
- [commaai.code](https://github.com/commaai) 
- [NervanaSystems.code](https://github.com/NervanaSystems) 
- [pytorch.code](https://github.com/pytorch) 
- [torch.code](https://github.com/torch) 
- [automl.code](https://github.com/automl) 
- [chainer.code](https://github.com/chainer) 
- [dmlc.code](https://github.com/dmlc) 
- [BVLC.code](https://github.com/BVLC) 
- [caffe2.code](https://github.com/caffe2) 
- [aimacode.code](https://github.com/aimacode) 
- [opencog.code](https://github.com/opencog) 
- [opencv.code](https://github.com/opencv) 
- [oxford.deepnlp](https://github.com/oxford-cs-deepnlp-2017) 
- [USC.ds.code](https://github.com/USC.ds.ir) 
- [gensim.nlp.ir](https://github.com/RaRe-Technologies/gensim) 
- [scikit.learn.code](https://github.com/scikit-learn) 
- [tesseract.code](https://github.com/tesseract-ocr/tesseract) 
- [ds.ipython](https://github.com/donnemartin/data-science-ipython-notebooks)
- [ml.book](https://github.com/rasbt/python-machine-learning-book) 
- [ccs.video.cources](https://github.com/Developer-Y/cs-video-courses) 
- [eff.tf](https://github.com/vahidk/EffectiveTensorflow) 
- [ml.tut](https://github.com/ujjwalkarn/Machine-Learning-Tutorials) 
- [swift.ai](https://github.com/Swift-AI) 
- [tf.book](https://github.com/BinRoot/TensorFlow-Book) 
- [ml.mindmaps](https://github.com/dformoso/machine-learning-mindmap) 
- [dl](https://github.com/Spandan-Madan/DeepLearningProject) 
- [h2o.ai](https://github.com/h2oai) 
- [fast.ai](https://github.com/fastai) 
- [tf.tut](https://github.com/Hvass-Labs/TensorFlow-Tutorials) 
- [tf.tut](https://github.com/Hvass-Labs/TensorFlow-Tutorials) 
- [PipelineAI](https://github.com/PipelineAI)
- [PRML](https://github.com/PRML)
- [mlpack](https://github.com/mlpack)
- [mit.nlp](https://github.com/mit-nlp)
- [scikit.learn.contrib](https://github.com/scikit-learn-contrib) 
- [mit.ml.pdf](https://github.com/janishar/mit-deep-learning-book-pdf)
- [ml.py.book](https://github.com/rasbt/python-machine-learning-book) 
- [onnx.code](https://github.com/onnx) 
- [aml.sf](https://en.wikipedia.org/wiki/Category:Applied_machine_learning) 
- [dmml.sf](https://en.wikipedia.org/wiki/Category:Data_mining_and_machine_learning_software) 
- [prolog.ai](https://www.prlog.org/news/tag/ai/#)
- [deep.rl.1](https://github.com/pemami4911/deep-rl) 
- [deep.rl.pytorch](https://github.com/ShangtongZhang/DeepRL) 
- [dl.tut](https://github.com/xiaohu2015/DeepLearning_tutorials)
- [dl.trade](https://github.com/Rachnog/Deep-Trading)
- [benchm.ml](https://github.com/szilard/benchm-ml)
- [dl.java](https://github.com/ivan-vasilev/neuralnetworks)
- [dl.tf](https://github.com/blackecho/Deep-Learning-TensorFlow)
- [self-car.ai](https://github.com/JianyangZhang/Self-Driving-Car-AI)
- [dql](https://github.com/blakeMilner/DeepQLearning) 
- [drl.berkeley](https://github.com/txizzle/drl) 
- [PAIR-code](https://github.com/PAIR-code)
- [fast.ai](http://www.fast.ai/)
- [courses.fast.ai](http://course.fast.ai/)
- [cs231n.stanford.edu/](http://cs231n.stanford.edu/) 
- [cs224d.stanford.edu/](http://cs224d.stanford.edu/) 
- [oxford-cs-deepnlp-2017](https://github.com/oxford-cs-deepnlp-2017/lectures) 
-  [UFLDL Tutorial 1](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)
-  [UFLDL Tutorial 2](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/)
- [dl.NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial)|
- [dl.p.dn](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks)
-  [dl.bu](http://www.metacademy.org/roadmaps/rgrosse/deep_learning)
- [Theano.tut](http://deeplearning.net/tutorial/deeplearning.pdf)
-  [nn.Matlab](http://uk.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf)
- [cnn.face](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/)
-  [Torch7.tut](https://github.com/clementfarabet/ipam-tutorials/tree/master/th_tutorials)
-  [ml.tut.web](https://github.com/josephmisiti/machine-learning-module)
- [VGG.cnn](http://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html)
- [TensorFlow.tut](https://github.com/nlintz/TensorFlow-Tutorials)
- [TensorFlow.tut+](https://github.com/pkmital/tensorflow_tutorials)
- [TensorFlow.py.n](https://github.com/aymericdamien/TensorFlow-Examples)
- [Keras.Lasagne.tut](https://github.com/Vict0rSch/deep_learning)
- [TensorFlow.LSTM.RNN](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition)
- [TensorFlow.World](https://github.com/astorfi/TensorFlow-World)
- [dl.py](https://www.manning.com/books/deep-learning-with-python)
- [Grokking.dl](https://www.manning.com/books/grokking-deep-learning) 
- [dl.Search](https://www.manning.com/books/deep-learning-for-search)
- [Keras.tut](https://blog.sicara.com/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511) 
- [Pytorch.tut](https://github.com/yunjey/pytorch-tutorial) 
- [Ray Kurzweil ](https://www.youtube.com/watch?v=RIkxVci-R4k)  
- [Andrew Ng](https://www.youtube.com/watch?v=n1ViNeWhC24) 
- [Geoff Hinton](https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) 
- [Yann LeCun](https://www.youtube.com/watch?v=sc-KbuZqGkI) 
- [Yoshua bengio](https://www.youtube.com/watch?v=4xsVFLnHC_0)  
- [Jeff Hawkins ](https://www.youtube.com/watch?v=6ufPpZDmPKA)  
- [Adam Coates ](https://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT)  
-  [Adam Coates ](http://vimeo.com/80821560)  
- [Adam Coates ](https://www.youtube.com/watch?v=wZfVBwOO0-k)  
-  [ Yann LeCun](https://www.youtube.com/watch?v=3boKlkPBckA)  
-  [Geoffrey Hinton at GoogleTechTalks ](https://www.youtube.com/watch?v=AyzOUbkUf3M)  
-  [Jeremy Howard at TEDxBrussels](http://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn)  
-  [ Andrew Ng in Stanford (2011)](http://web.stanford.edu/class/cs294a/handouts.html)  
- [ Chris Manning in Stanford ](http://web.stanford.edu/class/cs224n/handouts/)  
-  [ Natalie Hammel and Lorraine Yurshansky](http://googleresearch.blogspot.com/2015/09/a-beginners-guide-to-deep-neural.html)  
- [Steve Jurvetson (and panel) at VLAB in Stanford](https://www.youtube.com/watch?v=czLI3oLDe8M)   
- [Leo Isikdogan at Motorola Mobility HQ](https://www.youtube.com/watch?v=FoO8qDB8gUU) 
- [CS224u: Natural Language Understanding](https://github.com/cgpotts/cs224u)
- [Deep Learning – a cookbook view](http://on-demand.gputechconf.com/gtc-eu/2017/presentation/23245-sorin-cheran-hpes-deep-learning-cookbook.pdf)


------------------


**ML/DL conferences in 2018**

* [aideadlin.es](https://aideadlin.es/)
* [/r/MachineLearning/](https://www.reddit.com/r/MachineLearning/)
* [conference paper templates](https://github.com/wk910930/paper-template)
* [AI Conferences in 2018](https://blog.standuply.com/200-ai-ml-conferences-in-2018-eec7d0a50bcf)
* [Calendar of Computer Image Analysis, Computer Vision Conferences](http://conferences.visionbib.com/Iris-Conferences.html)
* [guide2research](http://www.guide2research.com/topconf/machine-learning)

**Conference Papers:**

- [JMLR Papers list](http://www.jmlr.org/papers/)
- [JAIR Paper list](https://www.jair.org/index.php/jair/issue/archive)
- [Google Scholar AI track](https://scholar.google.co.in/citations?view_op=top_venues&hl=en&vq=eng_artificialintelligence)
- [KDD explorations](http://www.kdd.org/explorations)
- [CVPR Open Access](http://openaccess.thecvf.com/menu.py)


-------

![ai_talent](https://github.com/gopala-kr/summary/blob/master/ai_talent.PNG)

----------------------
-------------------------------

[highscalability](http://highscalability.com/) - to dive in [all time favorites list](http://highscalability.com/all-time-favorites/)

* [[Week-1](https://github.com/gopala-kr/summary/blob/master/highscalability/Week-1/README.md)] 19-Nov-2017
* [[Week-2](https://github.com/gopala-kr/summary/blob/master/highscalability/Week-2/README.md)] 26-Nov-2017
* [[Week-3](https://github.com/gopala-kr/summary/blob/master/highscalability/Week-3/README.md)] 03-Dec-2017
* [[Week-4](https://github.com/gopala-kr/summary/tree/master/highscalability/week-4)] 10-Dec-2017
* [[Week-5](https://github.com/gopala-kr/summary/blob/master/highscalability/week-5/README.md)] 17-Dec-2017
* [[Week-6](https://github.com/gopala-kr/summary/blob/master/highscalability/Week-6/README.md)] 24-Dec-2017
* [[Week-7](https://github.com/gopala-kr/summary/tree/master/highscalability/week-7)] 31-Dec-2017
* [[startupreadings](https://github.com/dennybritz/startupreadings)]
* [[awesome-scalability](https://github.com/binhnguyennus/awesome-scalability)]
*********************
| [Blockchain](#blockchain) | [Ethereum](#ethereum) | [Bitcoin](https://bitcoin.org/bitcoin.pdf) | [LTC](#ltc) | [Neo](#neo) | [QTUM](#qtum) | [UBIQ](#ubiq) |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | 

* [[Week-1](https://github.com/gopala-kr/summary/tree/master/blockchains/Week-1)] 19-Nov-2017 - decentralized systems(Blockchain)
* [[Week-2](https://github.com/gopala-kr/summary/tree/master/blockchains/Week-2)] 26-Nov-2017 - decentralized systems(Blockchain)
* [[Week-3](https://github.com/gopala-kr/summary/tree/master/blockchains/Week-3)] 03-Dec-2017 - bitcoin content
* [[Week-4](https://github.com/gopala-kr/summary/tree/master/blockchains/week-4)] 17-Dec-2017 - bitcoin code
* [[Week-5](https://github.com/gopala-kr/summary/blob/master/blockchains/Week-4/README.md)] 24-Dec-2017 - ethereum content
* [[Week-6](https://github.com/gopala-kr/summary/tree/master/blockchains/Week-6)] 31-Dec-2017 - ethereum programming + ICO

-------------

|| [blockchain-papers](https://github.com/decrypto-org/blockchain-papers) | [blockchain in action](https://github.com/gopala-kr/weekend-with-github/tree/master/Projects-Blogs/09-blockchain-in-action) | [awesome-blockchain](https://github.com/igorbarinov/awesome-blockchain)+[influencers](https://www.rise.global/top-fintech-people)+[whitepapers](https://github.com/kasketis/awesome-cryptocurrencies)+[cryptocurrency](https://github.com/cmahon/awesome-cryptocurrency) | [ICO](http://www.coincatalyst.com/ultimate-guide-to-initial-coin-offerings-research-reading-list/)+[Resources](http://www.jeremydjacksonphd.com/tag/crypto/)|| 

----------------------
------------------

## Maintainer

Gopala KR / @gopala-kr