3 citations · 3 across the 1 of their papers we have counts for
3 papers
cs.LG2025
Querying Kernel Methods Suffices for Reconstructing their Training Data
Daniel Barzilai, Yuval Margalit, Eitan Gronich +3
Over-parameterized models have raised concerns about their potential to memorize training data, even when achieving strong generalization. The privacy implications of such memoriza…
cs.LG2024
On the Reconstruction of Training Data from Group Invariant Networks
Ran Elbaz, Gilad Yehudai, Meirav Galun +1
Reconstructing training data from trained neural networks is an active area of research with significant implications for privacy and explainability. Recent advances have demonstra…
cs.CV2012★ 3 cited
A Unified Multiscale Framework for Discrete Energy Minimization
Shai Bagon, Meirav Galun
Discrete energy minimization is a ubiquitous task in computer vision, yet is NP-hard in most cases. In this work we propose a multiscale framework for coping with the NP-hardness o…