7 citations · 32 across the 16 of their papers we have counts for
17 papers
Practical -Approximation for Submodular Maximization Subject to a Cardinality Constraint
Murad Tukan, Loay Mualem, Moran Feldman
Non-monotone constrained submodular maximization plays a crucial role in various machine learning applications. However, existing algorithms often struggle with a trade-off between…
Bridging the Gap Between General and Down-Closed Convex Sets in Submodular Maximization
Loay Mualem, Murad Tukan, Moran Fledman
Optimization of DR-submodular functions has experienced a notable surge in significance in recent times, marking a pivotal development within the domain of non-convex optimization.…
ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration
Murad Tukan, Fares Fares, Yotam Grufinkle +4
Navigating toy drones through uncharted GPS-denied indoor spaces poses significant difficulties due to their reliance on GPS for location determination. In such circumstances, the…
Dataset Distillation Meets Provable Subset Selection
Murad Tukan, Alaa Maalouf, Margarita Osadchy
Deep learning has grown tremendously over recent years, yielding state-of-the-art results in various fields. However, training such models requires huge amounts of data, increasing…
On the Size and Approximation Error of Distilled Sets
Alaa Maalouf, Murad Tukan, Noel Loo +3
Dataset Distillation is the task of synthesizing small datasets from large ones while still retaining comparable predictive accuracy to the original uncompressed dataset. Despite s…
AutoCoreset: An Automatic Practical Coreset Construction Framework
Alaa Maalouf, Murad Tukan, Vladimir Braverman +1
A coreset is a tiny weighted subset of an input set, that closely resembles the loss function, with respect to a certain set of queries. Coresets became prevalent in machine learni…
Provable Data Subset Selection For Efficient Neural Network Training
Murad Tukan, Samson Zhou, Alaa Maalouf +3
Radial basis function neural networks (\emph{RBFNN}) are {well-known} for their capability to approximate any continuous function on a closed bounded set with arbitrary precision g…
An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields
Murad Tukan, Eli Biton, Roee Diamant
Water current prediction is essential for understanding ecosystems, and to shed light on the role of the ocean in the global climate context. Solutions vary from physical modeling,…
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…
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 …
On Coresets for Support Vector Machines
Murad Tukan, Cenk Baykal, Dan Feldman +1
We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, represent…