558 citations · 1.1k across the 22 of their papers we have counts for
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Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding
Noam Levi, Alon Beck, Yohai Bar-Sinai
Grokking is the intriguing phenomenon where a model learns to generalize long after it has fit the training data. We show both analytically and numerically that grokking can surpri…
Hidden Markov modeling of single particle diffusion with stochastic tethering
Amit Federbush, Amit Moscovich, Yohai Bar-Sinai
The statistics of the diffusive motion of particles often serve as an experimental proxy for their interaction with the environment. However, inferring the physical properties from…
Charting the Topography of the Neural Network Landscape with Thermal-Like Noise
Theo Jules, Gal Brener, Tal Kachman +2
The training of neural networks is a complex, high-dimensional, non-convex and noisy optimization problem whose theoretical understanding is interesting both from an applicative pe…