Pierre H. Richemond

17 papers A* 2Journal 15

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	ShiQ: Bringing back Bellman to LLMs. CoRR Pierre Clavier, Nathan Grinsztajn, Raphaël Avalos, Yannis Flet-Berliac, Irem Ergün, Omar Darwiche Domingues, Eugene Tarassov, Olivier Pietquin, Pierre H. Richemond, Florian Strub, Matthieu Geist
2023	J	`jnl`	SemPPL: Predicting pseudo-labels for better contrastive representations. CoRR Matko Bosnjak, Pierre H. Richemond, Nenad Tomasev, Florian Strub, Jacob C. Walker, Felix Hill, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic
2023	J	`jnl`	The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. CoRR Pierre H. Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill
2022	J	`jnl`	Categorical SDEs with Simplex Diffusion. CoRR Pierre H. Richemond, Sander Dieleman, Arnaud Doucet
2022	J	`jnl`	Continuous diffusion for categorical data. CoRR Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, Jonas Adler
2022	A*	`conf`	Data Distributional Properties Drive Emergent In-Context Learning in Transformers. NeurIPS Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya K. Singh, Pierre H. Richemond, James L. McClelland, Felix Hill
2022	J	`jnl`	Data Distributional Properties Drive Emergent In-Context Learning in Transformers. CoRR Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya K. Singh, Pierre H. Richemond, Jay McClelland, Felix Hill
2022	J	`jnl`	Zipfian environments for Reinforcement Learning. CoRR Stephanie C. Y. Chan, Andrew K. Lampinen, Pierre H. Richemond, Felix Hill
2020	J	`jnl`	BYOL works even without batch statistics. CoRR Pierre H. Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel L. Smith, Soham De, Razvan Pascanu, Bilal Piot, Michal Valko
2020	A*	`conf`	Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. NeurIPS Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko
2020	J	`jnl`	Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. CoRR Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko
2019	J	`jnl`	Biologically inspired architectures for sample-efficient deep reinforcement learning. CoRR Pierre H. Richemond, Arinbjörn Kolbeinsson, Yike Guo
2019	J	`jnl`	Combining learning rate decay and weight decay with complexity gradient descent - Part I. CoRR Pierre H. Richemond, Yike Guo
2019	J	`jnl`	Static Activation Function Normalization. CoRR Pierre H. Richemond, Yike Guo
2017	J	`jnl`	A short variational proof of equivalence between policy gradients and soft Q learning. CoRR Pierre H. Richemond, Brendan Maginnis
2017	J	`jnl`	Efficiently applying attention to sequential data with the Recurrent Discounted Attention unit. CoRR Brendan Maginnis, Pierre H. Richemond
2017	J	`jnl`	On Wasserstein Reinforcement Learning and the Fokker-Planck equation. CoRR Pierre H. Richemond, Brendan Maginnis