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S. V. Kozyrev, I. A. Lopatin, Alexander Pechen
· 1 min read
ResearcharXiv cs.LG
Generalization in Learning: Eyring Formula and Predator-prey Model
arXiv:2610.35965v1 Announce Type: new
Abstract: In this work, generalization in machine learning is investigated using a predator-prey model. We overview biological and chemical interpretation of the stochastic gradient Langevin dynamics and generative adversarial network. For a particular example of recovering an unknown function by a polynomial of fixed degree from a set of noisy data, the predator-prey model provides better approximation compared to the gradient descent method. Further, thermodynamical arguments, in particular Eyring formula, are used to explain grokking (delayed generalization) phenomenon in machine learning.
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This story was published by arXiv cs.LG and written by S. V. Kozyrev, I. A. Lopatin, Alexander Pechen. SyncAI.news shows a preview; the complete article is on the publisher's site.
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