1.3k citations · 1.7k across the 63 of their papers we have counts for
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Signal Reconstruction from Modulo Observations
Viraj Shah, Chinmay Hegde
We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known…
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
Rahul Singh, Viraj Shah, Balaji Pokuri +3
A key problem in computational material science deals with understanding the effect of material distribution (i.e., microstructure) on material performance. The challenge is to syn…
Algorithmic Aspects of Inverse Problems Using Generative Models
Chinmay Hegde
The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled…
Phase Retrieval for Signals in Union of Subspaces
M. Salman Asif, Chinmay Hegde
We consider the phase retrieval problem for signals that belong to a union of subspaces. We assume that amplitude measurements of the signal of length are observed after passin…
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…
Autoencoders Learn Generative Linear Models
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
We provide a series of results for unsupervised learning with autoencoders. Specifically, we study shallow two-layer autoencoder architectures with shared weights. We focus on thre…