An Ensemble Approach to Multi-label Classification of Textual Data. - Karol Kurach; Krzysztof Pawlowski; Lukasz Romaszko; Marcin Tatjewski; Andrzej Janusz; Hung Son Nguyen (ADMA, 2012)

Multi-label Classification of Biomedical Articles. - Karol Kurach; Krzysztof Pawlowski; Lukasz Romaszko; Marcin Tatjewski; Andrzej Janusz; Hung Son Nguyen (Intelligent Tools for Building a Scientific Information Platform, 2013)

Coalition structure generation with the graphics processing unit. - Krzysztof Pawlowski; Karol Kurach; Kim Svensson; Sarvapali D. Ramchurn; Tomasz P. Michalak; Talal Rahwan (AAMAS, 2014)

Learning to Discover Efficient Mathematical Identities. - Wojciech Zaremba; Karol Kurach; Rob Fergus (NIPS, 2014)

Detecting Hazardous Events from Sequential Data with Multilayer Architectures. - Karol Kurach; Krzysztof Pawlowski (CS&P, 2015)

Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks. - Krzysztof Pawlowski; Karol Kurach (RSFDGrC, 2015)

Adding Gradient Noise Improves Learning for Very Deep Networks. - Arvind Neelakantan; Luke Vilnis; Quoc V. Le; Ilya Sutskever; Lukasz Kaiser; Karol Kurach; James Martens (CoRR, 2015)

Predicting Dangerous Seismic Activity with Recurrent Neural Networks. - Karol Kurach; Krzysztof Pawlowski (FedCSIS, 2016)

Smart Reply: Automated Response Suggestion for Email. - Anjuli Kannan; Karol Kurach; Sujith Ravi; Tobias Kaufmann 004; Andrew Tomkins; Balint Miklos; Greg Corrado; László Lukács; Marina Ganea; Peter Young; Vivek Ramavajjala (KDD, 2016)

Learning Efficient Algorithms with Hierarchical Attentive Memory. - Marcin Andrychowicz; Karol Kurach (CoRR, 2016)

Neural Random Access Machines. - Karol Kurach; Marcin Andrychowicz; Ilya Sutskever (ERCIM News, 2016)

Toward Optimal Run Racing: Application to Deep Learning Calibration. - Olivier Bousquet; Sylvain Gelly; Karol Kurach; Marc Schoenauer; Michèle Sebag; Olivier Teytaud; Damien Vincent (CoRR, 2017)

Critical Hyper-Parameters: No Random, No Cry. - Olivier Bousquet; Sylvain Gelly; Karol Kurach; Olivier Teytaud; Damien Vincent (CoRR, 2017)

Better Text Understanding Through Image-To-Text Transfer. - Karol Kurach; Sylvain Gelly; Michal Jastrzebski; Philip Häusser; Olivier Teytaud; Damien Vincent; Olivier Bousquet (CoRR, 2017)

Are GANs Created Equal? A Large-Scale Study. - Mario Lucic; Karol Kurach; Marcin Michalski; Sylvain Gelly; Olivier Bousquet (NeurIPS, 2018)

MemGEN: Memory is All You Need. - Sylvain Gelly; Karol Kurach; Marcin Michalski; Xiaohua Zhai (CoRR, 2018)

The GAN Landscape: Losses, Architectures, Regularization, and Normalization. - Karol Kurach; Mario Lucic; Xiaohua Zhai; Marcin Michalski; Sylvain Gelly (CoRR, 2018)

A Case for Object Compositionality in Deep Generative Models of Images. - Sjoerd van Steenkiste; Karol Kurach; Sylvain Gelly (CoRR, 2018)

Towards Accurate Generative Models of Video: A New Metric & Challenges. - Thomas Unterthiner; Sjoerd van Steenkiste; Karol Kurach; Raphaël Marinier; Marcin Michalski; Sylvain Gelly (CoRR, 2018)

FVD: A new Metric for Video Generation. - Thomas Unterthiner; Sjoerd van Steenkiste; Karol Kurach; Raphaël Marinier; Marcin Michalski; Sylvain Gelly (DGS@ICLR, 2019)

A Large-Scale Study on Regularization and Normalization in GANs. - Karol Kurach; Mario Lucic; Xiaohua Zhai; Marcin Michalski; Sylvain Gelly (ICML, 2019)

Google Research Football: A Novel Reinforcement Learning Environment. - Karol Kurach; Anton Raichuk; Piotr Stanczyk; Michal Zajac 005; Olivier Bachem; Lasse Espeholt; Carlos Riquelme; Damien Vincent; Marcin Michalski; Olivier Bousquet; Sylvain Gelly (AAAI, 2020)

Adversarial autoencoders for compact representations of 3D point clouds. - Maciej Zamorski; Maciej Zieba; Piotr Klukowski; Rafal Nowak; Karol Kurach; Wojciech Stokowiec; Tomasz Trzcinski (Comput. Vis. Image Underst., 2020)

Investigating object compositionality in Generative Adversarial Networks. - Sjoerd van Steenkiste; Karol Kurach; Jürgen Schmidhuber; Sylvain Gelly (Neural Networks, 2020)