🏢 Weizmann Institute of Science

This study proves that combining labeled and unlabeled data significantly improves high-dimensional sparse Gaussian classification, offering a polynomial-time SSL algorithm that outperforms supervised…

MALT: a novel adversarial attack, is 5x faster than AutoAttack, achieving higher success rates on CIFAR-100 and ImageNet by exploiting mesoscopic almost linearity in neural networks.

First-order methods conquer linearly constrained bilevel optimization, achieving near-optimal convergence rates and enhancing high-dimensional applicability.

Biologically-inspired AI model integrates learning & visual guidance via a novel ‘Counter-Hebb’ learning mechanism, achieving competitive performance on multi-task learning benchmarks.