# MLPapers

MLPapers is a collection of regularly updated reviews of machine learning research. Each topic connects methods, papers and implementations, summarizing how methods work, their assumptions, and what later findings extend or challenge.

## Topics

* [Anomaly detection](https://mlpapers.org/anomaly-detection/)
* [AutoML](https://mlpapers.org/automl/)
* [Bayesian inference](https://mlpapers.org/bayesian-inference/)
* [Bayesian networks](https://mlpapers.org/bayesian-nets/)
* [Causal inference](https://mlpapers.org/causal-inference/)
* [Clustering](https://mlpapers.org/clustering/)
* [Computer vision](https://mlpapers.org/computer-vision/)
* [Continual learning](https://mlpapers.org/continual-learning/)
* [Distributed learning](https://mlpapers.org/distributed-learning/)
* [Ensemble learning](https://mlpapers.org/ensemble-learning/)
* [Evaluation](https://mlpapers.org/evaluation/)
* [Feature extraction](https://mlpapers.org/feature-extraction/)
* [Feature selection](https://mlpapers.org/feature-selection/)
* [Federated learning](https://mlpapers.org/federated-learning/)
* [Generalization](https://mlpapers.org/generalization/)
* [Generative models](https://mlpapers.org/generative-models/)
* [Geometric deep learning](https://mlpapers.org/geometric-deep-learning/)
* [Graph neural networks](https://mlpapers.org/graph-neural-networks/)
* [Interpretability](https://mlpapers.org/interpretability/)
* [Machine unlearning](https://mlpapers.org/machine-unlearning/)
* [Meta-learning](https://mlpapers.org/meta-learning/)
* [Missing data](https://mlpapers.org/missing-data/)
* [Model predictive control](https://mlpapers.org/model-predictive-control/)
* [Models](https://mlpapers.org/models/)
* [Multiview learning](https://mlpapers.org/multiview-learning/)
* [Natural language processing](https://mlpapers.org/nlp/)
* [Neural networks](https://mlpapers.org/neural-nets/)
* [Neurosymbolic learning](https://mlpapers.org/neurosymbolic-learning/)
* [Optimization](https://mlpapers.org/optimization/)
* [Privacy-preserving learning](https://mlpapers.org/privacy-preserving-learning/)
* [Reinforcement learning](https://mlpapers.org/reinforcement-learning/)
* [Scientific machine learning](https://mlpapers.org/scientific-machine-learning/)
* [Symbolic regression](https://mlpapers.org/symbolic-regression/)
* [Time series forecasting](https://mlpapers.org/time-series-forecasting/)
* [Transfer learning](https://mlpapers.org/transfer-learning/)