2 citations · 2 across the 3 of their papers we have counts for
4 papers
Fast One-Pass Sparse Approximation of the Top Eigenvectors of Huge Approximately Low-Rank Matrices? Yes, !
Edem Boahen, Simone Brugiapaglia, Hung-Hsu Chou +2
Motivated by applications such as sparse PCA, in this paper we present provably-accurate one-pass algorithms for the sparse approximation of the top eigenvectors of extremely massi…
Deep greedy unfolding: Sorting out argsorting in greedy sparse recovery algorithms
Sina Mohammad-Taheri, Matthew J. Colbrook, Simone Brugiapaglia
Gradient-based learning imposes (deep) neural networks to be differentiable at all steps. This includes model-based architectures constructed by unrolling iterations of an iterativ…
A practical existence theorem for reduced order models based on convolutional autoencoders
Nicola Rares Franco, Simone Brugiapaglia
In recent years, deep learning has gained increasing popularity in the fields of Partial Differential Equations (PDEs) and Reduced Order Modeling (ROM), providing domain practition…
The greedy side of the LASSO: New algorithms for weighted sparse recovery via loss function-based orthogonal matching pursuit
Sina Mohammad-Taheri, Simone Brugiapaglia
We propose a class of greedy algorithms for weighted sparse recovery by considering new loss function-based generalizations of Orthogonal Matching Pursuit (OMP). Given a (regulariz…