Discovering Sparse Recovery Algorithms Using Neural Architecture Search
Abstract
The design of novel algorithms for solving inverse problems in signal processing is an incredibly difficult, heuristic-driven, and time-consuming task. In this short paper, we the idea of automated algorithm discovery in the signal processing context through meta-learning tools such as Neural Architecture Search (NAS). Specifically, we examine the Iterative Shrinkage Thresholding Algorithm (ISTA) and its accelerated Fast ISTA (FISTA) variant as candidates for algorithm rediscovery. We develop a meta-learning framework which is capable of rediscovering (several key elements of) the two aforementioned algorithms when given a search space of over 50,000 variables. We then show how our framework can apply to various data distributions and algorithms besides ISTA/FISTA.
Cite
@article{arxiv.2512.21563,
title = {Discovering Sparse Recovery Algorithms Using Neural Architecture Search},
author = {Patrick Yubeaton and Sarthak Gupta and M. Salman Asif and Chinmay Hegde},
journal= {arXiv preprint arXiv:2512.21563},
year = {2025}
}
Comments
Presented at the 59th Asilomar Conference on Signals, Systems, and Computers