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05/26/2022
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
This paper considers the Pointer Value Retrieval (PVR) benchmark introdu...
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02/15/2022
On the Origins of the Block Structure Phenomenon in Neural Network Representations
Recent work has uncovered a striking phenomenon in large-capacity neural...
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08/19/2021
Do Vision Transformers See Like Convolutional Neural Networks?
Convolutional neural networks (CNNs) have so far been the de-facto model...
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07/27/2021
Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization
The successes of deep learning critically rely on the ability of neural ...
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11/05/2020
Teaching with Commentaries
Effective training of deep neural networks can be challenging, and there...
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10/29/2020
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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07/14/2020
Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
A central challenge in developing versatile machine learning systems is ...
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03/26/2020
A Survey of Deep Learning for Scientific Discovery
Over the past few years, we have seen fundamental breakthroughs in core ...
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09/19/2019
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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03/28/2019
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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02/14/2019
Transfusion: Understanding Transfer Learning with Applications to Medical Imaging
With the increasingly varied applications of deep learning, transfer lea...
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07/04/2018
Direct Uncertainty Prediction with Applications to Healthcare
Large labeled datasets for supervised learning are frequently constructe...
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06/14/2018
Insights on representational similarity in neural networks with canonical correlation
Comparing different neural network representations and determining how r...
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01/09/2018
Adversarial Spheres
State of the art computer vision models have been shown to be vulnerable...
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11/07/2017
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Deep reinforcement learning has achieved many recent successes, but our ...
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06/19/2017
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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04/05/2017
Linear Additive Markov Processes
We introduce LAMP: the Linear Additive Markov Process. Transitions in LA...
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11/24/2016
Survey of Expressivity in Deep Neural Networks
We survey results on neural network expressivity described in "On the Ex...
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06/16/2016
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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06/16/2016
