The Matrix Calculus You Need For Deep Learning
Recent Advances in Deep Learning: An Overview
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
An Introduction to Deep Reinforcement Learning
Learning to Segment Every Thing
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
Adversarial Video Compression Guided by Soft Edge Detection
Deep Learned Frame Prediction for Video Compression
Bag of Tricks for Image Classification with Convolutional Neural Networks
Identifying and Correcting Label Bias in Machine Learning
Learning Not to Learn: Training Deep Neural Networks with Biased Data
These are only the papers I have found to be potentially interesting and are published.
Other papers that have not been published yet are Google DeepMind papers on Starcraft AI for imperfect information games and another paper on bioinformatics called AlphaFold.