160 citations · 267 across the 2 of their papers we have counts for
6 papers
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk +9
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple,…
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov +14
Representation learning promises to unlock deep learning for the long tail of vision tasks without expensive labelled datasets. Yet, the absence of a unified evaluation for general…
Google Research Football: A Novel Reinforcement Learning Environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk +8
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly te…
Towards Accurate Generative Models of Video: A New Metric & Challenges
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach +3
Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and repre…
A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lucic, Xiaohua Zhai +2
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully appli…
MemGEN: Memory is All You Need
Sylvain Gelly, Karol Kurach, Marcin Michalski +1
We propose a new learning paradigm called Deep Memory. It has the potential to completely revolutionize the Machine Learning field. Surprisingly, this paradigm has not been reinven…