paper-notes
Personal notes of deep learning and AI papers. See papers by topic.
Contributing
Please feel free to send pull requests of paper summaries or paper suggestions that you think are good!
By Date
09/2019
- On Inductive Biases in Deep Reinforcement Learning. Matteo Hessel, Hado van Hasselt, Joseph Modayil, David Silver (2019) [arxiv]
07/2018
- Learning to Search with MCTSnets. Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver (2018) [arxiv]
07/2017
- Proximal Policy Optimization. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov (2017) [pdf]
06/2017
- Making Humans a Multiplanetary Species. Elon Musk (2017) [pdf]
03/2017
- Lie-Access Neural Turing Machines. Greg Yang, Alexander M. Rush (2017) [pdf]
- Understanding Black-box Predictions via Influence Functions. Pang Wei Koh, Percy Liang (2017) [pdf]
11/2016
- DeepCoder: Learning to Write Programs. Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow (2016) [pdf]
- Image-to-Image Translation with Conditional Adversarial Networks. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros (2016) [pdf]
03/2015
- On Using Very Large Target Vocabulary for Neural Machine Translation. Sebastien Jean, Kyunghyun Cho, Roland Memisevic, Yoshua Bengio (2015) [pdf]
04/2014
- A Light Discussion and Derivation of Entropy. Jonathon Shlens (2014) [pdf]
- Notes on Kullback-Leibler Divergence and Likelihood Theory. Jonathon Shlens (2014) [pdf]