5 papers
Improving the affordability of robustness training for DNNs
Sidharth Gupta, Parijat Dube, Ashish Verma
Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational…
Fast Optical System Identification by Numerical Interferometry
Sidharth Gupta, Rémi Gribonval, Laurent Daudet +1
We propose a numerical interferometry method for identification of optical multiply-scattering systems when only intensity can be measured. Our method simplifies the calibration of…
Don't take it lightly: Phasing optical random projections with unknown operators
Sidharth Gupta, Rémi Gribonval, Laurent Daudet +1
In this paper we tackle the problem of recovering the phase of complex linear measurements when only magnitude information is available and we control the input. We are motivated b…
Solving Complex Quadratic Systems with Full-Rank Random Matrices
Shuai Huang, Sidharth Gupta, Ivan Dokmanić
We tackle the problem of recovering a complex signal from quadratic measurements of the form , where…
Random mesh projectors for inverse problems
Sidharth Gupta, Konik Kothari, Maarten V. de Hoop +1
We propose a new learning-based approach to solve ill-posed inverse problems in imaging. We address the case where ground truth training samples are rare and the problem is severel…