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Computer Science > Machine Learning

arXiv:2503.02113 (cs)
[Submitted on 3 Mar 2025]

Title:Deep Learning is Not So Mysterious or Different

Authors:Andrew Gordon Wilson
View a PDF of the paper titled Deep Learning is Not So Mysterious or
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    Abstract:Deep neural networks are often seen as different from
    other model classes by defying conventional notions of
    generalization. Popular examples of anomalous generalization
    behaviour include benign overfitting, double descent, and the
    success of overparametrization. We argue that these phenomena are
    not distinct to neural networks, or particularly mysterious.
    Moreover, this generalization behaviour can be intuitively
    understood, and rigorously characterized using long-standing
    generalization frameworks such as PAC-Bayes and countable
    hypothesis bounds. We present soft inductive biases as a key
    unifying principle in explaining these phenomena: rather than
    restricting the hypothesis space to avoid overfitting, embrace a
    flexible hypothesis space, with a soft preference for simpler
    solutions that are consistent with the data. This principle can
    be encoded in many model classes, and thus deep learning is not
    as mysterious or different from other model classes as it might
    seem. However, we also highlight how deep learning is relatively
    distinct in other ways, such as its ability for representation
    learning, phenomena such as mode connectivity, and its relative
    universality.

Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as:  arXiv:2503.02113 [cs.LG]
          (or arXiv:2503.02113v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2503.02113
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Andrew Wilson [view email]
[v1] Mon, 3 Mar 2025 22:56:04 UTC (1,206 KB)
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