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Related Experiment Videos

Deep neural nets as Hamiltonians.

Mike Winer1, Boris Hanin2

  • 1Institute for Advanced Study, University of Maryland, College Park, Maryland 20740, USA.

Physical Review. E
|February 20, 2026
PubMed
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This study views random neural networks as Hamiltonians, analyzing their energy landscapes. Infinite-width multilayer perceptrons exhibit complex behaviors, with some nonlinearities showing full replica symmetry breaking.

Area of Science:

  • Theoretical computer science
  • Statistical mechanics
  • Deep learning theory

Background:

  • Prior work in deep learning theory often analyzes network outputs over random parameters.
  • This study explores the inverse: the energy landscape of a fixed random network over its inputs.

Purpose of the Study:

  • To analyze the energy landscape of a randomly initialized multilayer perceptron (MLP) viewed as a Hamiltonian over its inputs.
  • To investigate the structure of near-global minima in the infinite-width limit.
  • To understand the behavior of different activation functions within this framework.

Main Methods:

  • Viewing a random MLP as a Hamiltonian over inputs.
  • Using the replica trick for exact analytic calculation of entropy (log volume).

Related Experiment Videos

  • Deriving and solving saddle-point equations for input overlaps using Gibbs distribution.
  • Numerical solutions for various depths and activation functions (tanh, sin, ReLU, shaped nonlinearities).

    • Main Results:

    • Exact analytic calculation of entropy at a given energy.
    • Derivation of saddle-point equations describing input overlaps.
    • Numerical solutions reveal diverse behaviors even at infinite width.
    • Full replica symmetry breaking observed for sin activation functions.
    • Replica symmetry observed for shallow tanh/ReLU and deep shaped MLPs.

    Conclusions:

    • Random MLPs exhibit rich energy landscape behaviors influenced by activation functions and depth.
    • Infinite-width networks can display complex statistical mechanics phenomena like replica symmetry breaking.
    • The Hamiltonian perspective provides new insights into deep learning theory and the properties of neural network landscapes.