Grokking as a Falsifiable Finite-Size Transition
Grokking as a Falsifiable Finite-Size Transition explores This paper explores the theoretical underpinnings of 'grokking' in machine learning, a phenomenon where models generalize after memorizing data, by applying statistical mechanics principles to understand its phase transition behavior.. Commercial viability score: 2/10 in LLM Training.
Updated 2 days ago
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