310 citations · 584 across the 20 of their papers we have counts for
6 papers · 1 filter
Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices
Chanwoo Chun, SueYeon Chung, Daniel D. Lee
Analyzing the structure of sampled features from an input data distribution is challenging when constrained by limited measurements in both the number of inputs and features. Tradi…
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks
Landan Seguin, Anthony Ndirango, Neeli Mishra +2
Adversarial defenses train deep neural networks to be invariant to the input perturbations from adversarial attacks. Almost all defense strategies achieve this invariance through a…
On the geometry of generalization and memorization in deep neural networks
Cory Stephenson, Suchismita Padhy, Abhinav Ganesh +3
Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance. To examine the structure of when and where memori…
On 1/n neural representation and robustness
Josue Nassar, Piotr Aleksander Sokol, SueYeon Chung +2
Understanding the nature of representation in neural networks is a goal shared by neuroscience and machine learning. It is therefore exciting that both fields converge not only on…
Untangling in Invariant Speech Recognition
Cory Stephenson, Jenelle Feather, Suchismita Padhy +4
Encouraged by the success of deep neural networks on a variety of visual tasks, much theoretical and experimental work has been aimed at understanding and interpreting how vision n…
Learning Data Manifolds with a Cutting Plane Method
SueYeon Chung, Uri Cohen, Haim Sompolinsky +1
We consider the problem of classifying data manifolds where each manifold represents invariances that are parameterized by continuous degrees of freedom. Conventional data augmenta…