18 citations · 69 across the 20 of their papers we have counts for
9 papers · 1 filter
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
Mohammadreza Soltani, Swayambhoo Jain, Abhinav Sambasivan
Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions. Most of the existing works implicitly as…
Fast Low-Rank Matrix Estimation without the Condition Number
Mohammadreza Soltani, Chinmay Hegde
In this paper, we study the general problem of optimizing a convex function over the set of matrices, subject to rank constraints on . However, existing firs…
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
Viraj Shah, Mohammadreza Soltani, Chinmay Hegde
We consider the problem of reconstructing signals and images from periodic nonlinearities. For such problems, we design a measurement scheme that supports efficient reconstruction;…
Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations
Mohammadreza Soltani, Chinmay Hegde
We consider the demixing problem of two (or more) structured high-dimensional vectors from a limited number of nonlinear observations where this nonlinearity is due to either a per…
Fast Algorithms for Learning Latent Variables in Graphical Models
Mohammadreza Soltani, Chinmay Hegde
We study the problem of learning latent variables in Gaussian graphical models. Existing methods for this problem assume that the precision matrix of the observed variables is the…
Improved Algorithms for Matrix Recovery from Rank-One Projections
Mohammadreza Soltani, Chinmay Hegde
We consider the problem of estimation of a low-rank matrix from a limited number of noisy rank-one projections. In particular, we propose two fast, non-convex \emph{proper} algorit…
Iterative Thresholding for Demixing Structured Superpositions in High Dimensions
Mohammadreza Soltani, Chinmay Hegde
We consider the demixing problem of two (or more) high-dimensional vectors from nonlinear observations when the number of such observations is far less than the ambient dimension o…
Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps
Mohammadreza Soltani, Chinmay Hegde
Random sinusoidal features are a popular approach for speeding up kernel-based inference in large datasets. Prior to the inference stage, the approach suggests performing dimension…
Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations
Mohammadreza Soltani, Chinmay Hegde
We study the problem of demixing a pair of sparse signals from noisy, nonlinear observations of their superposition. Mathematically, we consider a nonlinear signal observation mode…