Sparsity-aware generalization theory for deep neural networks

no code implementations • 1 Jul 2023 • Ramchandran Muthukumar, Jeremias Sulam

In this paper, we present a new approach to analyzing generalization for deep feed-forward ReLU networks that takes advantage of the degree of sparsity that is achieved in the hidden layer activations.

Adversarial robustness of sparse local Lipschitz predictors

no code implementations • 26 Feb 2022 • Ramchandran Muthukumar, Jeremias Sulam

This work studies the adversarial robustness of parametric functions composed of a linear predictor and a non-linear representation map.

Adversarial Robustness

Adversarial Robustness of Supervised Sparse Coding

1 code implementation • NeurIPS 2020 • Jeremias Sulam, Ramchandran Muthukumar, Raman Arora

Several recent results provide theoretical insights into the phenomena of adversarial examples.

Adversarial Robustness