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Computer Science > Machine Learning

arXiv:2401.14953 (cs)
[Submitted on 26 Jan 2024]

Title:Learning Universal Predictors

Authors:Jordi Grau-Moya, Tim Genewein, Marcus Hutter, Laurent Orseau,
Gregoire Deletang, Elliot Catt, Anian Ruoss, Li Kevin Wenliang,
Christopher Mattern, Matthew Aitchison, Joel Veness
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Jordi Grau-Moya and 10 other authors
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    Abstract:Meta-learning has emerged as a powerful approach to
    train neural networks to learn new tasks quickly from limited
    data. Broad exposure to different tasks leads to versatile
    representations enabling general problem solving. But, what are
    the limits of meta-learning? In this work, we explore the
    potential of amortizing the most powerful universal predictor,
    namely Solomonoff Induction (SI), into neural networks via
    leveraging meta-learning to its limits. We use Universal Turing
    Machines (UTMs) to generate training data used to expose networks
    to a broad range of patterns. We provide theoretical analysis of
    the UTM data generation processes and meta-training protocols. We
    conduct comprehensive experiments with neural architectures (e.g.
    LSTMs, Transformers) and algorithmic data generators of varying
    complexity and universality. Our results suggest that UTM data is
    a valuable resource for meta-learning, and that it can be used to
    train neural networks capable of learning universal prediction
    strategies.

Comments: 32 pages, 11 figures
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2401.14953 [cs.LG]
          (or arXiv:2401.14953v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2401.14953
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jordi Grau-Moya [view email]
[v1] Fri, 26 Jan 2024 15:37:16 UTC (1,097 KB)
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