[Submitted on 25 Oct 2026]
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.
Submission history
From: Sergei Kozyrev [view email]
[v1] Sun, 25 Oct 2026 14:22:21 UTC (69 KB)
