1 citations · 4 across the 6 of their papers we have counts for
6 papers
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.…
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,…