Ilya Sutskever
Ilya Sutskever
Co-Founder and Chief Scientist of OpenAI
Vahvistettu sähköpostiosoite verkkotunnuksessa openai.com - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Imagenet classification with deep convolutional neural networks
A Krizhevsky, I Sutskever, GE Hinton
Advances in neural information processing systems 25, 1097-1105, 2012
102418*2012
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ...
arXiv preprint arXiv:1603.04467, 2016
34433*2016
Dropout: a simple way to prevent neural networks from overfitting
N Srivastava, G Hinton, A Krizhevsky, I Sutskever, R Salakhutdinov
The journal of machine learning research 15 (1), 1929-1958, 2014
333392014
Distributed representations of words and phrases and their compositionality
T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean
Advances in neural information processing systems, 3111-3119, 2013
315642013
Sequence to sequence learning with neural networks
I Sutskever, O Vinyals, QV Le
Advances in neural information processing systems, 3104-3112, 2014
175482014
Mastering the game of Go with deep neural networks and tree search
D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ...
nature 529 (7587), 484-489, 2016
122072016
Intriguing properties of neural networks
C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ...
arXiv preprint arXiv:1312.6199, 2013
91022013
Improving neural networks by preventing co-adaptation of feature detectors
GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov
arXiv preprint arXiv:1207.0580, 2012
73412012
On the importance of initialization and momentum in deep learning
I Sutskever, J Martens, G Dahl, G Hinton
International conference on machine learning, 1139-1147, 2013
41802013
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel
Proceedings of the 30th International Conference on Neural Information …, 2016
3780*2016
Improving language understanding by generative pre-training
A Radford, K Narasimhan, T Salimans, I Sutskever
29862018
Language models are few-shot learners
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
arXiv preprint arXiv:2005.14165, 2020
27082020
Language models are unsupervised multitask learners
A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever
OpenAI blog 1 (8), 9, 2019
23742019
Recurrent neural network regularization
W Zaremba, I Sutskever, O Vinyals
arXiv preprint arXiv:1409.2329, 2014
23132014
An empirical exploration of recurrent network architectures
R Jozefowicz, W Zaremba, I Sutskever
International conference on machine learning, 2342-2350, 2015
17062015
Generating text with recurrent neural networks
I Sutskever, J Martens, GE Hinton
ICML, 2011
15682011
Exploiting similarities among languages for machine translation
T Mikolov, QV Le, I Sutskever
arXiv preprint arXiv:1309.4168, 2013
13922013
Improved variational inference with inverse autoregressive flow
DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling
Advances in neural information processing systems 29, 4743-4751, 2016
12692016
Evolution strategies as a scalable alternative to reinforcement learning
T Salimans, J Ho, X Chen, S Sidor, I Sutskever
arXiv preprint arXiv:1703.03864, 2017
10172017
Grammar as a foreign language
O Vinyals, Ł Kaiser, T Koo, S Petrov, I Sutskever, G Hinton
Advances in neural information processing systems 28, 2773-2781, 2015
9702015
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Artikkelit 1–20