9 citations · 9 across the 2 of their papers we have counts for
6 papers
Adversarial Token Attacks on Vision Transformers
Ameya Joshi, Gauri Jagatap, Chinmay Hegde
Vision transformers rely on a patch token based self attention mechanism, in contrast to convolutional networks. We investigate fundamental differences between these two families o…
Provable Compressed Sensing with Generative Priors via Langevin Dynamics
Thanh V. Nguyen, Gauri Jagatap, Chinmay Hegde
Deep generative models have emerged as a powerful class of priors for signals in various inverse problems such as compressed sensing, phase retrieval and super-resolution. Here, we…
Adversarially Robust Learning via Entropic Regularization
Gauri Jagatap, Ameya Joshi, Animesh Basak Chowdhury +2
In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additi…
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
Gauri Jagatap, Chinmay Hegde
Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafte…
Learning ReLU Networks via Alternating Minimization
Gauri Jagatap, Chinmay Hegde
We propose and analyze a new family of algorithms for training neural networks with ReLU activations. Our algorithms are based on the technique of alternating minimization: estimat…
Sample-Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements
Gauri Jagatap, Chinmay Hegde
We consider the problem of recovering a signal , from magnitude-only measurements for $i…