Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial
It provides a cross-disciplinary tutorial for researchers, but it is incremental as it reviews existing concepts without presenting new results.
This tutorial explores the intersection of statistical mechanics and machine learning, aiming to enhance model efficiency and robustness by applying principles like entropy and free energy to inspire new methodologies in uncertain contexts.
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.