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arXiv:2402.06184 (cs)
[Submitted on 9 Feb 2024]

Title:The boundary of neural network trainability is fractal

Authors:Jascha Sohl-Dickstein
Download a PDF of the paper titled The boundary of neural network
trainability is fractal, by Jascha Sohl-Dickstein
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    Abstract:Some fractals -- for instance those associated with the
    Mandelbrot and quadratic Julia sets -- are computed by iterating
    a function, and identifying the boundary between hyperparameters
    for which the resulting series diverges or remains bounded.
    Neural network training similarly involves iterating an update
    function (e.g. repeated steps of gradient descent), can result in
    convergent or divergent behavior, and can be extremely sensitive
    to small changes in hyperparameters. Motivated by these
    similarities, we experimentally examine the boundary between
    neural network hyperparameters that lead to stable and divergent
    training. We find that this boundary is fractal over more than
    ten decades of scale in all tested configurations.

Comments: 3 pages, mesmerizing fractals
Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing
          (cs.NE); Chaotic Dynamics (nlin.CD)
Cite as:  arXiv:2402.06184 [cs.LG]
          (or arXiv:2402.06184v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2402.06184
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jascha Sohl-Dickstein [view email]
[v1] Fri, 9 Feb 2024 04:46:48 UTC (36,948 KB)
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