arXiv Machine Learning By Eugene Golikov, Yaroslav Gusev, Dmitry Yarotsky

A prism hierarchy of learning regimes in large linear autoencoders

Read the original on arXiv Machine Learning →

arXiv:2606. 05335v1 Announce Type: new Abstract: Theoretical studies of machine learning models commonly consider different limiting regimes in which the learning dynamics of gradient descent becomes theoretically tractable.

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arXiv AI
Jun 29

Derivation of effective gradient flow equations and dynamical truncation of training data in Deep Learning

arXiv:2501. 07400v2 Announce Type: replace-cross Abstract: We derive explicit equations governing the cumulative biases and weights in Deep Learning with ReLU activation function, based on gradient descent for the Euclidean loss in the input layer, and under the assumption that the weights are, in a precise sense, adapted to the coordinate system distinguished by the activations.

By Thomas Chen
arXiv Machine Learning
Sep 10

The Dynamics of Generalization in Deep Learning

arXiv:2504.16450v4 Announce Type: replace Abstract: We derive a differential equation that governs the evolution of the generalization gap when a model is trained by gradient descent-based methods. T...

By Rubing Yang, Pratik Chaudhari