11 citations · 14 across the 4 of their papers we have counts for
4 papers
Just How Flexible are Neural Networks in Practice?
Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal +3
It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters, underpinning notions of overparameterized and underpara…
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations
Rylan Schaeffer, Victor Lecomte, Dhruv Bhandarkar Pai +10
Maximum Manifold Capacity Representations (MMCR) is a recent multi-view self-supervised learning (MVSSL) method that matches or surpasses other leading MVSSL methods. MMCR is intri…
Reverse Engineering Self-Supervised Learning
Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti +2
Self-supervised learning (SSL) is a powerful tool in machine learning, but understanding the learned representations and their underlying mechanisms remains a challenge. This paper…
What Do We Maximize in Self-Supervised Learning?
Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun
In this paper, we examine self-supervised learning methods, particularly VICReg, to provide an information-theoretical understanding of their construction. As a first step, we demo…