
Teaching with Commentaries
Effective training of deep neural networks can be challenging, and there...
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Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
A key factor in the success of deep neural networks is the ability to sc...
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Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
A central challenge in developing versatile machine learning systems is ...
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A Survey of Deep Learning for Scientific Discovery
Over the past few years, we have seen fundamental breakthroughs in core ...
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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 ...
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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...
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Transfusion: Understanding Transfer Learning with Applications to Medical Imaging
With the increasingly varied applications of deep learning, transfer lea...
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Direct Uncertainty Prediction with Applications to Healthcare
Large labeled datasets for supervised learning are frequently constructe...
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Insights on representational similarity in neural networks with canonical correlation
Comparing different neural network representations and determining how r...
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Adversarial Spheres
State of the art computer vision models have been shown to be vulnerable...
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Can Deep Reinforcement Learning Solve ErdosSelfridgeSpencer Games?
Deep reinforcement learning has achieved many recent successes, but our ...
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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...
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Linear Additive Markov Processes
We introduce LAMP: the Linear Additive Markov Process. Transitions in LA...
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Survey of Expressivity in Deep Neural Networks
We survey results on neural network expressivity described in "On the Ex...
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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...
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On the Expressive Power of Deep Neural Networks
We propose a new approach to the problem of neural network expressivity,...
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Maithra Raghu
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