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What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study


Jun 10, 2020
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphael Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem

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SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference


Oct 15, 2019
Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, Marcin Michalski

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The Visual Task Adaptation Benchmark


Oct 01, 2019
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby

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Google Research Football: A Novel Reinforcement Learning Environment


Jul 25, 2019
Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

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Towards Accurate Generative Models of Video: A New Metric & Challenges


Dec 03, 2018
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, Sylvain Gelly

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Are GANs Created Equal? A Large-Scale Study


Oct 29, 2018
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet

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* NIPS'18: Added a section on the limitations of the study and additional empirical results 

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The GAN Landscape: Losses, Architectures, Regularization, and Normalization


Oct 26, 2018
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly

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* Changed formatting from ICML workshop to ICLR. We added additional resnet ablation studies, hinge loss, and an empirical comparison between KID and FID 

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MemGEN: Memory is All You Need


Mar 29, 2018
Sylvain Gelly, Karol Kurach, Marcin Michalski, Xiaohua Zhai

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