4 citations · 4 across the 7 of their papers we have counts for
6 papers · 1 filter
STARFISH: faST Accuracy Recovery in pruned networks From Internal State Healing
Shir Maon, Odelia Melamed, Adi Shamir
Pruning is a process designed to reduce the number of weights in a large neural network. This can substantially speed up inference but might cause a considerable reduction in the m…
Provable Unlearning with Gradient Ascent on Two-Layer ReLU Neural Networks
Odelia Melamed, Gilad Yehudai, Gal Vardi
Machine Unlearning aims to remove specific data from trained models, addressing growing privacy and ethical concerns. We provide a theoretical analysis of a simple and widely used…
MALT Powers Up Adversarial Attacks
Odelia Melamed, Gilad Yehudai, Adi Shamir
Current adversarial attacks for multi-class classifiers choose the target class for a given input naively, based on the classifier's confidence levels for various target classes. W…
Explaining high-dimensional text classifiers
Odelia Melamed, Rich Caruana
Explainability has become a valuable tool in the last few years, helping humans better understand AI-guided decisions. However, the classic explainability tools are sometimes quite…
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
Odelia Melamed, Gilad Yehudai, Gal Vardi
Despite a great deal of research, it is still not well-understood why trained neural networks are highly vulnerable to adversarial examples. In this work we focus on two-layer neur…
The Dimpled Manifold Model of Adversarial Examples in Machine Learning
Adi Shamir, Odelia Melamed, Oriel BenShmuel
The extreme fragility of deep neural networks, when presented with tiny perturbations in their inputs, was independently discovered by several research groups in 2013. However, des…