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Maithra Raghu
Maithra Raghu
Cornell University and Google Brain
Verified email at cornell.edu - Homepage
Title
Cited by
Cited by
Year
Transfusion: Understanding transfer learning for medical imaging
M Raghu, C Zhang, J Kleinberg, S Bengio
Advances in neural information processing systems 32, 2019
7012019
On the expressive power of deep neural networks
M Raghu, B Poole, J Kleinberg, S Ganguli, J Sohl-Dickstein
international conference on machine learning, 2847-2854, 2017
6472017
Exponential expressivity in deep neural networks through transient chaos
B Poole, S Lahiri, M Raghu, J Sohl-Dickstein, S Ganguli
Advances in neural information processing systems 29, 2016
4432016
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
M Raghu, J Gilmer, J Yosinski, J Sohl-Dickstein
Advances in neural information processing systems 30, 2017
4242017
Rapid learning or feature reuse? towards understanding the effectiveness of maml
A Raghu, M Raghu, S Bengio, O Vinyals
arXiv preprint arXiv:1909.09157, 2019
3492019
Adversarial spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
319*2018
Insights on representational similarity in neural networks with canonical correlation
A Morcos, M Raghu, S Bengio
Advances in Neural Information Processing Systems 31, 2018
2362018
Do vision transformers see like convolutional neural networks?
M Raghu, T Unterthiner, S Kornblith, C Zhang, A Dosovitskiy
Advances in Neural Information Processing Systems 34, 2021
1532021
A survey of deep learning for scientific discovery
M Raghu, E Schmidt
arXiv preprint arXiv:2003.11755, 2020
125*2020
Direct uncertainty prediction for medical second opinions
M Raghu, K Blumer, R Sayres, Z Obermeyer, B Kleinberg, S Mullainathan, ...
International Conference on Machine Learning, 5281-5290, 2019
74*2019
Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth
T Nguyen, M Raghu, S Kornblith
arXiv preprint arXiv:2010.15327, 2020
702020
The algorithmic automation problem: Prediction, triage, and human effort
M Raghu, K Blumer, G Corrado, J Kleinberg, Z Obermeyer, ...
arXiv preprint arXiv:1903.12220, 2019
602019
Anatomy of catastrophic forgetting: Hidden representations and task semantics
VV Ramasesh, E Dyer, M Raghu
arXiv preprint arXiv:2007.07400, 2020
532020
Team performance with test scores
J Kleinberg, M Raghu
ACM Transactions on Economics and Computation (TEAC) 6 (3-4), 1-26, 2018
322018
Can deep reinforcement learning solve Erdos-Selfridge-Spencer games?
M Raghu, A Irpan, J Andreas, B Kleinberg, Q Le, J Kleinberg
International Conference on Machine Learning, 4238-4246, 2018
232018
Teaching with commentaries
A Raghu, M Raghu, S Kornblith, D Duvenaud, G Hinton
arXiv preprint arXiv:2011.03037, 2020
152020
Linear additive markov processes
R Kumar, M Raghu, T Sarlós, A Tomkins
Proceedings of the 26th international conference on World Wide Web, 411-419, 2017
102017
Explaining the learning dynamics of direct feedback alignment
J Gilmer, C Raffel, SS Schoenholz, M Raghu, J Sohl-Dickstein
52017
Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization
C Zhang, M Raghu, J Kleinberg, S Bengio
arXiv preprint arXiv:2107.12580, 2021
32021
Identifying and understanding deep learning phenomena
H Sedghi, S Bengio, K Hata, A Madry, A Morcos, B Neyshabur, M Raghu, ...
ICML 2019 Workshop, 2019
22019
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Articles 1–20