# Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial

arXiv: https://arxiv.org/abs/2411.15945 · HTML: https://mlsanity.com/paper/2411.15945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 24 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning](https://mlsanity.com/paper/1912.02729/) (2019)

## Abstract (arXiv)

This tutorial investigates the convergence of statistical mechanics and learning theory, elucidating the potential enhancements in machine learning methodologies through the integration of foundational principles from physics. The tutorial delves into advanced techniques like entropy, free energy, and variational inference which are utilized in machine learning, illustrating their significant contributions to model efficiency and robustness. By bridging these scientific disciplines, we aspire to inspire newer methodologies in researches, demonstrating how an in-depth comprehension of physical systems' behavior can yield more effective and dependable machine learning models, particularly in contexts characterized by uncertainty.