Следене
Karol Kurach
Karol Kurach
Google Brain
Потвърден имейл адрес: google.com
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Позовавания
Позовавания
Година
Are gans created equal? a large-scale study
M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet
Advances in neural information processing systems 31, 2018
9012018
Adding gradient noise improves learning for very deep networks
A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens
arXiv preprint arXiv:1511.06807, 2015
4722015
Smart reply: Automated response suggestion for email
A Kannan, K Kurach, S Ravi, T Kaufmann, A Tomkins, B Miklos, ...
Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016
3082016
Neural random-access machines
K Kurach, M Andrychowicz, I Sutskever
arXiv preprint arXiv:1511.06392, 2015
1762015
Towards accurate generative models of video: A new metric & challenges
T Unterthiner, S Van Steenkiste, K Kurach, R Marinier, M Michalski, ...
arXiv preprint arXiv:1812.01717, 2018
1662018
Google research football: A novel reinforcement learning environment
K Kurach, A Raichuk, P Stańczyk, M Zając, O Bachem, L Espeholt, ...
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4501-4510, 2020
1542020
A large-scale study on regularization and normalization in GANs
K Kurach, M Lučić, X Zhai, M Michalski, S Gelly
International conference on machine learning, 3581-3590, 2019
1542019
The gan landscape: Losses, architectures, regularization, and normalization
K Kurach, M Lucic, X Zhai, M Michalski, S Gelly
1392018
Adversarial autoencoders for compact representations of 3D point clouds
M Zamorski, M Zięba, P Klukowski, R Nowak, K Kurach, W Stokowiec, ...
Computer Vision and Image Understanding 193, 102921, 2020
802020
Are GANs created equal
M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet
A large-scale study. arXiv e-prints 2 (4), 2017
712017
Learning to discover efficient mathematical identities
W Zaremba, K Kurach, R Fergus
Advances in Neural Information Processing Systems 27, 2014
582014
Learning efficient algorithms with hierarchical attentive memory
M Andrychowicz, K Kurach
arXiv preprint arXiv:1602.03218, 2016
532016
Investigating object compositionality in generative adversarial networks
S Van Steenkiste, K Kurach, J Schmidhuber, S Gelly
Neural Networks 130, 309-325, 2020
312020
Critical hyper-parameters: No random, no cry
O Bousquet, S Gelly, K Kurach, O Teytaud, D Vincent
arXiv preprint arXiv:1706.03200, 2017
282017
FVD: A new metric for video generation
T Unterthiner, S van Steenkiste, K Kurach, R Marinier, M Michalski, ...
242019
Adding gradient noise improves learning for very deep networks. arXiv 2015
A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens
arXiv preprint arXiv:1511.06807, 2020
192020
A case for object compositionality in deep generative models of images
S van Steenkiste, K Kurach, S Gelly
162018
Coalition structure generation with the graphics processing unit
K Pawłowski, K Kurach, K Svensson, S Ramchurn, TP Michalak, ...
Proc. AAMAS, 293-300, 2014
132014
Predicting dangerous seismic activity with recurrent neural networks
K Kurach, K Pawlowski
2016 Federated Conference on Computer Science and Information Systems …, 2016
112016
Detecting methane outbreaks from time series data with deep neural networks
K Pawłowski, K Kurach
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 15th …, 2015
112015
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