Maithra Raghu

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Google ∙ MIT

A Survey of Deep Learning for Scientific Discovery

Over the past few years, we have seen fundamental breakthroughs in core ...
03/26/2020 ∙ by Maithra Raghu, et al. ∙ 76 ∙ share

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Transfusion: Understanding Transfer Learning with Applications to Medical Imaging

With the increasingly varied applications of deep learning, transfer lea...
02/14/2019 ∙ by Maithra Raghu, et al. ∙ 26 ∙ share

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The Algorithmic Automation Problem: Prediction, Triage, and Human Effort

In a wide array of areas, algorithms are matching and surpassing the per...
03/28/2019 ∙ by Maithra Raghu, et al. ∙ 8 ∙ share

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

An important research direction in machine learning has centered around ...
09/19/2019 ∙ by Aniruddh Raghu, et al. ∙ 5 ∙ share

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

Deep reinforcement learning has achieved many recent successes, but our ...
11/07/2017 ∙ by Maithra Raghu, et al. ∙ 0 ∙ share

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Survey of Expressivity in Deep Neural Networks

We survey results on neural network expressivity described in "On the Ex...
11/24/2016 ∙ by Maithra Raghu, et al. ∙ 0 ∙ share

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

We propose a new technique, Singular Vector Canonical Correlation Analys...
06/19/2017 ∙ by Maithra Raghu, et al. ∙ 0 ∙ share

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Linear Additive Markov Processes

We introduce LAMP: the Linear Additive Markov Process. Transitions in LA...
04/05/2017 ∙ by Ravi Kumar, et al. ∙ 0 ∙ share

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Exponential expressivity in deep neural networks through transient chaos

We combine Riemannian geometry with the mean field theory of high dimens...
06/16/2016 ∙ by Ben Poole, et al. ∙ 0 ∙ share

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On the Expressive Power of Deep Neural Networks

We propose a new approach to the problem of neural network expressivity,...
06/16/2016 ∙ by Maithra Raghu, et al. ∙ 0 ∙ share

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Adversarial Spheres

State of the art computer vision models have been shown to be vulnerable...
01/09/2018 ∙ by Justin Gilmer, et al. ∙ 0 ∙ share

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Insights on representational similarity in neural networks with canonical correlation

Comparing different neural network representations and determining how r...
06/14/2018 ∙ by Ari S. Morcos, et al. ∙ 0 ∙ share

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Direct Uncertainty Prediction with Applications to Healthcare

Large labeled datasets for supervised learning are frequently constructe...
07/04/2018 ∙ by Maithra Raghu, et al. ∙ 0 ∙ share

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Maithra Raghu

Featured Co-authors

Quoc V. Le

Oriol Vinyals

Ian Goodfellow

Jascha Sohl-Dickstein

Samy Bengio

Jon Kleinberg

Surya Ganguli

Jason Yosinski

Jacob Andreas

Ben Poole

Samuel S. Schoenholz

A Survey of Deep Learning for Scientific Discovery

Transfusion: Understanding Transfer Learning with Applications to Medical Imaging

The Algorithmic Automation Problem: Prediction, Triage, and Human Effort

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

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

Survey of Expressivity in Deep Neural Networks

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

Linear Additive Markov Processes

Exponential expressivity in deep neural networks through transient chaos

On the Expressive Power of Deep Neural Networks

Adversarial Spheres

Insights on representational similarity in neural networks with canonical correlation

Direct Uncertainty Prediction with Applications to Healthcare