Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912131785424896 |
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| author | Star Liu |
| author_facet | Star Liu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15945 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial Star Liu Machine Learning Materials Science Statistics Theory Chemical Physics 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. |
| title | Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial |
| topic | Machine Learning Materials Science Statistics Theory Chemical Physics |
| url | https://arxiv.org/abs/2411.15945 |