251 citations · 833 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
Toward Optimal Run Racing: Application to Deep Learning Calibration
Olivier Bousquet, Sylvain Gelly, Karol Kurach +4
This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural arc…
Critical Hyper-Parameters: No Random, No Cry
Olivier Bousquet, Sylvain Gelly, Karol Kurach +2
The selection of hyper-parameters is critical in Deep Learning. Because of the long training time of complex models and the availability of compute resources in the cloud, "one-sho…
Learning Efficient Algorithms with Hierarchical Attentive Memory
Marcin Andrychowicz, Karol Kurach
In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM). It is based on a binary tree with leaves corre…
Neural Random-Access Machines
Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine. It can manipulate and dereference pointers to an external variable-…
Learning to Discover Efficient Mathematical Identities
Wojciech Zaremba, Karol Kurach, Rob Fergus
In this paper we explore how machine learning techniques can be applied to the discovery of efficient mathematical identities. We introduce an attribute grammar framework for repre…