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

• URL: http://arxiv.org/abs/2411.15945v1
• Date: Sun, 24 Nov 2024 18:20:05 GMT
• Title: Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial
• Authors: Star, Liu,
• Abstract summary: The tutorial delves into advanced techniques like entropy, free energy, and variational inference which are utilized in machine learning. We show how an in-depth comprehension of physical systems' behavior can yield more effective and dependable machine learning models.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Causal Inference Tools for a Better Evaluation of Machine Learning [0.0]
  We introduce key statistical methods such as Ordinary Least Squares (OLS) regression, Analysis of Variance (ANOVA) and logistic regression. The document serves as a guide for researchers and practitioners, detailing how these techniques can provide deeper insights into model behavior, performance, and fairness.
  arXiv  Detail & Related papers  (2024-10-02T10:03:29Z)
• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Interpretable Meta-Learning of Physical Systems [4.343110120255532]
  Recent meta-learning methods rely on black-box neural networks, resulting in high computational costs and limited interpretability. We argue that multi-environment generalization can be achieved using a simpler learning model, with an affine structure with respect to the learning task. We demonstrate the competitive generalization performance and the low computational cost of our method by comparing it to state-of-the-art algorithms on physical systems.
  arXiv  Detail & Related papers  (2023-12-01T10:18:50Z)
• A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
  We propose a general bi-level probabilistic graphical reasoning framework called GBPGR. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• Machine Psychology [54.287802134327485]
  We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology. We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
  arXiv  Detail & Related papers  (2023-03-24T13:24:41Z)
• Symmetry Group Equivariant Architectures for Physics [52.784926970374556]
  In the domain of machine learning, an awareness of symmetries has driven impressive performance breakthroughs. We argue that both the physics community and the broader machine learning community have much to understand.
  arXiv  Detail & Related papers  (2022-03-11T18:27:04Z)
• Measuring and modeling the motor system with machine learning [117.44028458220427]
  The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. We discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems.
  arXiv  Detail & Related papers  (2021-03-22T12:42:16Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Physics guided machine learning using simplified theories [0.0]
  Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. We introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines.
  arXiv  Detail & Related papers  (2020-12-18T21:30:40Z)
• Using machine-learning modelling to understand macroscopic dynamics in a system of coupled maps [0.0]
  We consider a case study the macroscopic motion emerging from a system of globally coupled maps. We build a coarse-grained Markov process for the macroscopic dynamics both with a machine learning approach and with a direct numerical computation of the transition probability of the coarse-grained process. We are able to infer important information about the effective dimension of the attractor, the persistence of memory effects and the multi-scale structure of the dynamics.
  arXiv  Detail & Related papers  (2020-11-08T15:38:12Z)
• Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles [0.0]
  We demonstrate an unsupervised learning approach that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems.
  arXiv  Detail & Related papers  (2020-03-03T18:37:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.