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.