7 citations · 22 across the 7 of their papers we have counts for
9 papers
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Murad Tukan, Loay Mualem, Alaa Maalouf
Pruning is one of the predominant approaches for compressing deep neural networks (DNNs). Lately, coresets (provable data summarizations) were leveraged for pruning DNNs, adding th…
New Coresets for Projective Clustering and Applications
Murad Tukan, Xuan Wu, Samson Zhou +2
-projective clustering is the natural generalization of the family of -clustering and -subspace clustering problems. Given a set of points in , the g…
Obstacle Aware Sampling for Path Planning
Murad Tukan, Alaa Maalouf, Dan Feldman +1
Many path planning algorithms are based on sampling the state space. While this approach is very simple, it can become costly when the obstacles are unknown, since samples hitting…
Coresets for Data Discretization and Sine Wave Fitting
Alaa Maalouf, Murad Tukan, Eric Price +2
In the \emph{monitoring} problem, the input is an unbounded stream of integers in , that are obtained from a sensor (such as GPS or heart b…
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…