19 citations · 78 across the 31 of their papers we have counts for
6 papers · 1 filter
Enhancing Neural Training via a Correlated Dynamics Model
Jonathan Brokman, Roy Betser, Rotem Turjeman +3
As neural networks grow in scale, their training becomes both computationally demanding and rich in dynamics. Amidst the flourishing interest in these training dynamics, we present…
Minimizing Quotient Regularization Model
Chao Wang, Jean-Francois Aujol, Guy Gilboa +1
Quotient regularization models (QRMs) are a class of powerful regularization techniques that have gained considerable attention in recent years, due to their ability to handle comp…
Robustifying Point Cloud Networks by Refocusing
Meir Yossef Levi, Guy Gilboa
The ability to cope with out-of-distribution (OOD) corruptions and adversarial attacks is crucial in real-world safety-demanding applications. In this study, we develop a general m…
Additive Class Distinction Maps using Branched-GANs
Elnatan Kadar, Jonathan Brokman, Guy Gilboa
We present a new model, training procedure and architecture to create precise maps of distinction between two classes of images. The objective is to comprehend, in pixel-wise resol…
EPiC: Ensemble of Partial Point Clouds for Robust Classification
Meir Yossef Levi, Guy Gilboa
Robust point cloud classification is crucial for real-world applications, as consumer-type 3D sensors often yield partial and noisy data, degraded by various artifacts. In this wor…
Graph Laplacian for Semi-Supervised Learning
Or Streicher, Guy Gilboa
Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph (or nonlocal) Laplacian is a fundamental smooth…