Algorithmic Information Dynamics of Learning: A Certified, Differentiable Complexity Controller for Grokking
This paper introduces a certified, differentiable complexity controller based on Algorithmic Information Dynamics that accelerates the "grokking" phenomenon in neural networks by precisely timing intervention kicks, thereby validating a data-dependent Occam boundary and demonstrating that the method's primary contribution is optimal timing rather than feature attribution.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.