Linking Fundamental Physics to Deep Learning

Ever since Mehta and Schwab laid out the relationship between restricted Boltzmann machines (RBM) and deep learning mathematically (see my previous entry), scientists have been discussing why deep learning works so well. Recently, Henry Lin and Max Tegmark put a preprint on arXiv (arXiv:1609.09225), arguing that deep learning works because it captures a few essential physical laws and properties. Tegmark is a cosmologist.

Physical laws are simple in a way that a few properties, such as locality, symmetry, hierarchy etc., lead to large-scale, universal, and often complex phenomena. A lot of machine learning algorithms, including deep learning algorithms, have deep relations with formalisms outlined in statistical mechanics.

A lot of machine learning algorithms are basically probability theory. They outlined a few types of algorithms that seek various types of probabilities. They related the probabilities to Hamiltonians in many-body systems.

They argued why neural networks can approximate functions (polynomials) so well, giving a simple neural network performing multiplication. With central limit theorem or Jaynes’ arguments (see my previous entry), a lot of multiplications, they said, can be approximated by low-order polynomial Hamiltonian. This is like a lot of many-body systems that can be approximated by 4-th order Landau-Ginzburg-Wilson (LGW) functional.

Properties such as locality reduces the number of hyper-parameters needed because it restricts to interactions among close proximities. Symmetry further reduces it, and also computational complexities. Symmetry and second order phase transition make scaling hypothesis possible, leading to the use of the tools such as renormalization group (RG). As many people have been arguing, deep learning resembles RG because it filters out unnecessary information and maps out the crucial features. Tegmark use classifying cats vs. dogs as an example, as in retrieving temperatures of a many-body systems using RG procedure. They gave a counter-example to Schwab’s paper with the probabilities cannot be preserved by RG procedure, but while it is sound, but it is not the point of the RG procedure anyway.

They also discussed about the no-flattening theorems for neural networks.

  • Henry W. Lin, Max Tegmark, “Why does deep and cheap learning work so well?” arXiv:1608.08225 (2016). [arXiv]
  • Charles H. Martin, “on Cheap Learning: Partition Functions and RBMs,” Machine Learning, WordPress (2016). [WordPress]
  • “The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe,” MIT Technology Review: Computing (2016). [MITTechReview]
  • “Why is the Lin and Tegmark paper ‘Why does deep and cheap learning work so well?’ important?” [Quora]
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