251 citations · 833 across the 13 of their papers we have counts for
14 papers
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…
Investigating Object Compositionality in Generative Adversarial Networks
Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber +1
Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations…
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…
Are GANs Created Equal? A Large-Scale Study
Mario Lucic, Karol Kurach, Marcin Michalski +2
Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is stil…
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…
Better Text Understanding Through Image-To-Text Transfer
Karol Kurach, Sylvain Gelly, Michal Jastrzebski +4
Generic text embeddings are successfully used in a variety of tasks. However, they are often learnt by capturing the co-occurrence structure from pure text corpora, resulting in li…
Smart Reply: Automated Response Suggestion for Email
Anjuli Kannan, Karol Kurach, Sujith Ravi +8
In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply. It generates semantically diverse suggest…
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…
Adding Gradient Noise Improves Learning for Very Deep Networks
Arvind Neelakantan, Luke Vilnis, Quoc V. Le +4
Deep feedforward and recurrent networks have achieved impressive results in many perception and language processing applications. This success is partially attributed to architectu…
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…