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Samuel S. Schoenholz
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My research focuses on understanding neural networks using techniques from statistical physics, using machine learning to do science, and exploring differentiable programming by writing differentiable software.
Papers66 papers
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ANNUAL REVIEW OF CONDENSED MATTER PHYSICS, VOL 11, 2020, no. 1 (2020): 501-528
NIPS 2020, (2020)
Victor Bapst, Thomas Keck,Agnieszka Grabska-Barwinska, Craig Donner,Ekin Dogus Cubuk,Sam Schoenholz, Annette Obika, Alexander Nelson, Trevor Back,Demis Hassabis,Pushmeet Kohli
Bulletin of the American Physical Society, (2020)
ICML, pp.10462-10472, (2020)
V. Bapst, T. Keck, A. Grabska-Barwińska, C. Donner,E. D. Cubuk,S. S. Schoenholz, A. Obika, A. W. R. Nelson, T. Back, D. Hassabis,P. Kohli
Nature Physics, pp.1-1, (2020)
international conference on learning representations, (2020)
V. Bapst, T. Keck, A. Grabska-Barwińska, C. Donner,E. D. Cubuk,S. S. Schoenholz, A. Obika, A. W. R. Nelson, T. Back, D. Hassabis,P. Kohli
Nature Physics, no. 4 (2020): 448-454
Bulletin of the American Physical Society, (2020)
Physical review letters, no. 2 (2019): 028001
ICLR, (2019)
Bulletin of the American Physical Society, (2019)
arXiv: Learning, (2019)
Yann Dauphin,Samuel Schoenholz
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 12624-12636
Cited by5EIBibtex
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 8570-8581
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