The Kanerva Machine: A Generative Distributed Memory. - Yan Wu 010; Greg Wayne; Alex Graves; Timothy P. Lillicrap (ICLR (Poster), 2018)

Learning Attractor Dynamics for Generative Memory. - Yan Wu 010; Gregory Wayne; Karol Gregor; Timothy P. Lillicrap (NeurIPS, 2018)

Optimizing Agent Behavior over Long Time Scales by Transporting Value. - Chia-Chun Hung; Timothy P. Lillicrap; Josh Abramson; Yan Wu 010; Mehdi Mirza; Federico Carnevale; Arun Ahuja; Greg Wayne (CoRR, 2018)

Deep Compressed Sensing. - Yan Wu 010; Mihaela Rosca; Timothy P. Lillicrap (ICML, 2019)

Shaping Belief States with Generative Environment Models for RL. - Karol Gregor; Danilo Jimenez Rezende; Frederic Besse; Yan Wu 010; Hamza Merzic; Aäron van den Oord (NeurIPS, 2019)

LOGAN: Latent Optimisation for Generative Adversarial Networks. - Yan Wu 010; Jeff Donahue; David Balduzzi; Karen Simonyan; Timothy P. Lillicrap (CoRR, 2019)

Training Generative Adversarial Networks by Solving Ordinary Differential Equations. - Chongli Qin; Yan Wu 010; Jost Tobias Springenberg; Andy Brock; Jeff Donahue; Timothy P. Lillicrap; Pushmeet Kohli (NeurIPS, 2020)

Product Kanerva Machines: Factorized Bayesian Memory. - Adam H. Marblestone; Yan Wu 010; Greg Wayne (CoRR, 2020)

Discretization Drift in Two-Player Games. - Mihaela Rosca; Yan Wu 010; Benoit Dherin; David Barrett 001 (ICML, 2021)

Towards Learning Universal Audio Representations. - Luyu Wang; Pauline Luc; Yan Wu 010; Adrià Recasens; Lucas Smaira; Andrew Brock; Andrew Jaegle; Jean-Baptiste Alayrac; Sander Dieleman; João Carreira 001; Aäron van den Oord (ICASSP, 2022)

On a continuous time model of gradient descent dynamics and instability in deep learning. - Mihaela Rosca; Yan Wu 010; Chongli Qin; Benoit Dherin (Trans. Mach. Learn. Res., 2023)