Exponential expressivity in deep neural networks through transient chaos. - Ben Poole; Subhaneil Lahiri; Maithra Raghu; Jascha Sohl-Dickstein; Surya Ganguli (NIPS, 2016)

Survey of Expressivity in Deep Neural Networks. - Maithra Raghu; Ben Poole; Jon M. Kleinberg; Surya Ganguli; Jascha Sohl-Dickstein (CoRR, 2016)

Explaining the Learning Dynamics of Direct Feedback Alignment. - Justin Gilmer; Colin Raffel; Samuel S. Schoenholz; Maithra Raghu; Jascha Sohl-Dickstein (ICLR (Workshop), 2017)

On the Expressive Power of Deep Neural Networks. - Maithra Raghu; Ben Poole; Jon M. Kleinberg; Surya Ganguli; Jascha Sohl-Dickstein (ICML, 2017)

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. - Maithra Raghu; Justin Gilmer; Jason Yosinski; Jascha Sohl-Dickstein (NIPS, 2017)

Linear Additive Markov Processes. - Ravi Kumar 001; Maithra Raghu; Tamás Sarlós; Andrew Tomkins (WWW, 2017)

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. - Maithra Raghu; Justin Gilmer; Jason Yosinski; Jascha Sohl-Dickstein (CoRR, 2017)

Adversarial Spheres. - Justin Gilmer; Luke Metz; Fartash Faghri; Samuel S. Schoenholz; Maithra Raghu; Martin Wattenberg; Ian J. Goodfellow (ICLR (Workshop), 2018)

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? - Maithra Raghu; Alex Irpan; Jacob Andreas; Robert Kleinberg; Quoc V. Le; Jon M. Kleinberg (ICML, 2018)

Insights on representational similarity in neural networks with canonical correlation. - Ari S. Morcos; Maithra Raghu; Samy Bengio (NeurIPS, 2018)

Direct Uncertainty Prediction with Applications to Healthcare. - Maithra Raghu; Katy Blumer; Rory Sayres; Ziad Obermeyer; Sendhil Mullainathan; Jon M. Kleinberg (CoRR, 2018)

Team Performance with Test Scores. - Jon M. Kleinberg; Maithra Raghu (ACM Trans. Economics and Comput., 2018)

Direct Uncertainty Prediction for Medical Second Opinions. - Maithra Raghu; Katy Blumer; Rory Sayres; Ziad Obermeyer; Robert D. Kleinberg; Sendhil Mullainathan; Jon M. Kleinberg (ICML, 2019)

Transfusion: Understanding Transfer Learning for Medical Imaging. - Maithra Raghu; Chiyuan Zhang; Jon M. Kleinberg; Samy Bengio (NeurIPS, 2019)

Transfusion: Understanding Transfer Learning with Applications to Medical Imaging. - Maithra Raghu; Chiyuan Zhang; Jon M. Kleinberg; Samy Bengio (CoRR, 2019)

The Algorithmic Automation Problem: Prediction, Triage, and Human Effort. - Maithra Raghu; Katy Blumer; Greg Corrado; Jon M. Kleinberg; Ziad Obermeyer; Sendhil Mullainathan (CoRR, 2019)

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML. - Aniruddh Raghu; Maithra Raghu; Samy Bengio; Oriol Vinyals (ICLR, 2020)

A Survey of Deep Learning for Scientific Discovery. - Maithra Raghu; Eric Schmidt (CoRR, 2020)

Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth. - Thao Nguyen; Maithra Raghu; Simon Kornblith (ICLR, 2021)

Teaching with Commentaries. - Aniruddh Raghu; Maithra Raghu; Simon Kornblith; David Duvenaud; Geoffrey E. Hinton (ICLR, 2021)

Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics. - Vinay Venkatesh Ramasesh; Ethan Dyer; Maithra Raghu (ICLR, 2021)

Do Vision Transformers See Like Convolutional Neural Networks? - Maithra Raghu; Thomas Unterthiner; Simon Kornblith; Chiyuan Zhang; Alexey Dosovitskiy (NeurIPS, 2021)

Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization. - Chiyuan Zhang; Maithra Raghu; Jon M. Kleinberg; Samy Bengio (CoRR, 2021)

Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures. - Emmanuel Abbe; Samy Bengio; Elisabetta Cornacchia; Jon M. Kleinberg; Aryo Lotfi; Maithra Raghu; Chiyuan Zhang (NeurIPS, 2022)

On the Origins of the Block Structure Phenomenon in Neural Network Representations. - Thao Nguyen; Maithra Raghu; Simon Kornblith (Trans. Mach. Learn. Res., 2022)