ANALYSIS OF KINETIC MODELS FOR LABEL SWITCHING AND STOCHASTIC GRADIENT DESCENT

Burger M, Rossi A (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Kinetic and Related Models American Institute of Mathematical Sciences (AIMS)

DOI: 10.3934/krm.2023005

Abstract

In this paper we provide a novel approach to the analysis of kinetic models for label switching, which are used for particle systems that can randomly switch between gradient flows in different energy landscapes. Besides problems in biology and physics, we also demonstrate that stochastic gradient descent, the most popular technique in machine learning, can be understood in this setting, when considering a time-continuous variant. Our analysis is focusing on the case of evolution in a collection of external potentials, for which we provide analytical and numerical results about the evolution as well as the stationary problem.

Authors with CRIS profile

Martin Burger Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik) Alex Rossi Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

How to cite

APA:

Burger, M., & Rossi, A. (2023). ANALYSIS OF KINETIC MODELS FOR LABEL SWITCHING AND STOCHASTIC GRADIENT DESCENT. Kinetic and Related Models. https://dx.doi.org/10.3934/krm.2023005

MLA:

Burger, Martin, and Alex Rossi. "ANALYSIS OF KINETIC MODELS FOR LABEL SWITCHING AND STOCHASTIC GRADIENT DESCENT." Kinetic and Related Models (2023).

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