Maithra Raghu
Maithra Raghu
Cornell University and Google Brain
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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, 3360-3368, 2016
On the expressive power of deep neural networks
M Raghu, B Poole, J Kleinberg, S Ganguli, JS Dickstein
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
Transfusion: Understanding transfer learning for medical imaging
M Raghu, C Zhang, J Kleinberg, S Bengio
Advances in Neural Information Processing Systems, 3342-3352, 2019
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, 6072-6081, 2017
Adversarial Spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
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
Insights on representational similarity in neural networks with canonical correlation
A Morcos, M Raghu, S Bengio
Advances in Neural Information Processing Systems, 5727-5736, 2018
A Survey of Deep Learning for Scientific Discovery
M Raghu, E Schmidt
arXiv preprint arXiv:2003.11755, 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
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
Team performance with test scores
J Kleinberg, M Raghu
ACM Transactions on Economics and Computation (TEAC) 6 (3-4), 17, 2018
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
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
Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
VV Ramasesh, E Dyer, M Raghu
arXiv preprint arXiv:2007.07400, 2020
Linear Additive Markov Processes
R Kumar, M Raghu, T Sarlos, A Tomkins
arXiv preprint arXiv:1704.01255, 2017
Teaching with Commentaries
A Raghu, M Raghu, S Kornblith, D Duvenaud, G Hinton
arXiv preprint arXiv:2011.03037, 2020
Do Vision Transformers See Like Convolutional Neural Networks?
M Raghu, T Unterthiner, S Kornblith, C Zhang, A Dosovitskiy
arXiv preprint arXiv:2108.08810, 2021
Explaining the Learning Dynamics of Direct Feedback Alignment
J Gilmer, C Raffel, SS Schoenholz, M Raghu, J Sohl-Dickstein
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
Insights from Deep Representations for Machine Learning Systems and Human Collaborations
M Raghu
PQDT-UK & Ireland, 2020
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