509 citations · 546 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
A Unified Approach to Coreset Learning
Alaa Maalouf, Gilad Eini, Ben Mussay +2
Coreset of a given dataset and loss function is usually a small weighed set that approximates this loss for every query from a given set of queries. Coresets have shown to be very…
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…
Data-Independent Neural Pruning via Coresets
Ben Mussay, Margarita Osadchy, Vladimir Braverman +2
Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy. Model compression became a central research topic,…
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning
Jinchao Liu, Stuart J. Gibson, Margarita Osadchy
Deep Learning shows very good performance when trained on large labeled data sets. The problem of training a deep net on a few or one sample per class requires a different learning…
Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution
Jinchao Liu, Margarita Osadchy, Lorna Ashton +3
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non…
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples
Dolev Raviv, Margarita Osadchy
Deep Learning (DL) methods show very good performance when trained on large, balanced data sets. However, many practical problems involve imbalanced data sets, or/and classes with…