README.rst

References

Summary: This a is non-exhaustive list of references for this component.

Table of Contents

• Using model-based Reinforcement Learning
• Using Dynamical Systems
• Explainable AI (XAI)

Most of the methods below use Self-Supervised Learning (for the stage of Preprocessing) in order to achieve representation disentanglement.

Using model-based Reinforcement Learning

2018

• Recall Traces: Backtracking Models for Efficient Reinforcement Learning

2019

• Task-Agnostic Dynamics Priors for Deep Reinforcement Learning

Using Dynamical Systems

2016

• Kernel Learning for Dynamic Texture Synthesis

2019

• VideoBERT: A Joint Model for Video and Language Representation Learning
• Self-Supervised Learning by Cross-Modal Audio-Video Clustering
• Toward an AI Physicist for Unsupervised Learning

2020

• Discovering physical concepts with neural networks

2021

• Discovering State Variables Hidden in Experimental Data
• Nobel Turing Challenge: creating the engine for scientific discovery

2022

• Automated discovery of fundamental variables hidden in experimental data
• Discovering sparse interpretable dynamics from partial observations
• Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction

Explainable AI (XAI)

See also Awesome-explainable-AI and Awesome XAI.

2018

• Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)
• Explaining Explanations: An Overview of Interpretability of Machine Learning

2019

• Definitions, methods, and applications in interpretable machine learning
• Machine Learning Interpretability: A Survey on Methods and Metrics

2020

• Explainable Reinforcement Learning: A Survey
• Explainable Machine Learning for Scientific Insights and Discoveries

2021

• Principles and Practice of Explainable Machine Learning

2022

• A Survey of Explainable Reinforcement Learning