15 citations · 68 across the 22 of their papers we have counts for
5 papers · 1 filter
Deep Learning Meets Projective Clustering
Alaa Maalouf, Harry Lang, Daniela Rus +1
A common approach for compressing NLP networks is to encode the embedding layer as a matrix , compute its rank- approximation via SVD, and then…
Compressed Deep Networks: Goodbye SVD, Hello Robust Low-Rank Approximation
Murad Tukan, Alaa Maalouf, Matan Weksler +1
A common technique for compressing a neural network is to compute the -rank approximation of the matrix that corresponds to a ful…
Coresets for Near-Convex Functions
Murad Tukan, Alaa Maalouf, Dan Feldman
Coreset is usually a small weighted subset of input points in , that provably approximates their loss function for a given set of queries (models, classifiers, et…
Faster PAC Learning and Smaller Coresets via Smoothed Analysis
Alaa Maalouf, Ibrahim Jubran, Murad Tukan +1
PAC-learning usually aims to compute a small subset (-sample/net) from items, that provably approximates a given loss function for every query (model, classifier,…
Sets Clustering
Ibrahim Jubran, Murad Tukan, Alaa Maalouf +1
The input to the \emph{sets--means} problem is an integer and a set of sets in . The goal is to compute a set of …