Maithra Raghu

According to our database, Maithra Raghu authored at least 25 papers between 2016 and 2022.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2022

On the Origins of the Block Structure Phenomenon in Neural Network Representations.

Thao Nguyen, Maithra Raghu, Simon Kornblith

Trans. Mach. Learn. Res., 2022

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Do Vision Transformers See Like Convolutional Neural Networks?

Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics.

Vinay Venkatesh Ramasesh, Ethan Dyer, Maithra Raghu

Proceedings of the 9th International Conference on Learning Representations, 2021

Teaching with Commentaries.

Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

A Survey of Deep Learning for Scientific Discovery.

Maithra Raghu, Eric Schmidt

CoRR, 2020

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML.

Aniruddh Raghu, Maithra Raghu, Samy Bengio, Oriol Vinyals

Proceedings of the 8th International Conference on Learning Representations, 2020

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

Transfusion: Understanding Transfer Learning with Applications to Medical Imaging.

Maithra Raghu, Chiyuan Zhang, Jon M. Kleinberg, Samy Bengio

CoRR, 2019

Transfusion: Understanding Transfer Learning for Medical Imaging.

Maithra Raghu, Chiyuan Zhang, Jon M. Kleinberg, Samy Bengio

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Direct Uncertainty Prediction for Medical Second Opinions.

Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert D. Kleinberg, Sendhil Mullainathan, Jon M. Kleinberg

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Team Performance with Test Scores.

Jon M. Kleinberg, Maithra Raghu

ACM Trans. Economics and Comput., 2018

Direct Uncertainty Prediction with Applications to Healthcare.

Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Sendhil Mullainathan, Jon M. Kleinberg

CoRR, 2018

Insights on representational similarity in neural networks with canonical correlation.

Ari S. Morcos, Maithra Raghu, Samy Bengio

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Can Deep Reinforcement Learning solve Erdos-Selfridge-Spencer Games?

Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon M. Kleinberg

Proceedings of the 6th International Conference on Learning Representations, 2018

Adversarial Spheres.

Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement.

Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein

CoRR, 2017

Linear Additive Markov Processes.

Ravi Kumar, Maithra Raghu, Tamás Sarlós, Andrew Tomkins

Proceedings of the 26th International Conference on World Wide Web, 2017

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability.

Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On the Expressive Power of Deep Neural Networks.

Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein

Proceedings of the 34th International Conference on Machine Learning, 2017

Explaining the Learning Dynamics of Direct Feedback Alignment.

Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Survey of Expressivity in Deep Neural Networks.

Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein

CoRR, 2016

Exponential expressivity in deep neural networks through transient chaos.

Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016