34 citations · 269 across the 42 of their papers we have counts for
10 papers · 1 filter
Faster Projective Clustering Approximation of Big Data
Adiel Statman, Liat Rozenberg, Dan Feldman
In projective clustering we are given a set of n points in and wish to cluster them to a set of linear subspaces in according to some given distance function. A…
Introduction to Core-sets: an Updated Survey
Dan Feldman
In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over so…
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…
Data-Independent Structured Pruning of Neural Networks via Coresets
Ben Mussay, Daniel Feldman, Samson Zhou +2
Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the co…
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…