An Introduction to Machine Learning: a perspective from Statistical Physics

被引:9
|
作者
Decelle, Aurelien [1 ,2 ]
机构
[1] Univ Complutense, Dept Fis Teor, Madrid 28040, Spain
[2] Univ Paris Saclay, INRIA Tau team, CNRS, LISN, F-91190 Saclay, France
关键词
Machine Learning; Perceptron; Restricted Boltzmann Machine; Phase diagram; NETWORK; MODEL; STORAGE;
D O I
10.1016/j.physa.2022.128154
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The recent progresses in Machine Learning opened the door to actual applications of learning algorithms but also to new research directions both in the field of Machine Learning directly and, at the edges with other disciplines. The case that interests us is the interface with physics, and more specifically Statistical Physics. In this short lecture, I will try to present first a brief introduction to Machine Learning from the angle of neural networks. After explaining quickly some fundamental models and global aspects of the training procedure, I will discuss into more detail two examples illustrate what can be done from the Statistical Physics perspective. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页数:31
相关论文
共 50 条
  • [1] STATISTICAL PHYSICS OF LEARNING FROM EXAMPLES - A BRIEF INTRODUCTION
    VANDENBROECK, C
    ACTA PHYSICA POLONICA B, 1994, 25 (06): : 903 - 923
  • [2] Machine learning and statistical physics: preface
    Agliari, Elena
    Barra, Adriano
    Sollich, Peter
    Zdeborova, Lenka
    JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2020, 53 (50)
  • [3] Statistical physics of learning from examples. A brief introduction
    Van, den Broeck, C.
    Acta Physica Polonica, Series B: Particle Physics and Field Theory, Nuclear Physics Theory of Relativity, 1994, 25 (06):
  • [4] Machine learning renormalization group for statistical physics
    Hou, Wanda
    You, Yi-Zhuang
    MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2023, 4 (04):
  • [5] A simple guide from machine learning outputs to statistical criteria in particle physics
    Khosa, Charanjit Kaur
    Sanz, Veronica
    Soughton, Michael
    SCIPOST PHYSICS CORE, 2022, 5 (04):
  • [6] Integrating statistical physics and machine learning for combinatorial optimization
    Shen, Zi-Song
    Zhang, Pan
    NATURE COMPUTATIONAL SCIENCE, 2025, : 277 - 278
  • [7] Introduction to statistical physics outside physics
    Stauffer, D
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2004, 336 (1-2) : 1 - 5
  • [8] Sampling Algorithms in Statistical Physics: A Guide for Statistics and Machine Learning
    Faulkner, Michael F.
    Livingstone, Samuel
    STATISTICAL SCIENCE, 2024, 39 (01) : 137 - 164
  • [9] A Bayesian perspective of statistical machine learning for big data
    Rajiv Sambasivan
    Sourish Das
    Sujit K. Sahu
    Computational Statistics, 2020, 35 : 893 - 930
  • [10] A Bayesian perspective of statistical machine learning for big data
    Sambasivan, Rajiv
    Das, Sourish
    Sahu, Sujit K.
    COMPUTATIONAL STATISTICS, 2020, 35 (03) : 893 - 930