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Computer Science > Machine Learning

arXiv:2312.00276 (cs)
[Submitted on 1 Dec 2023]

Title:Automating Continual Learning

Authors:Kazuki Irie, Robert Csordas, Jurgen Schmidhuber
Download a PDF of the paper titled Automating Continual Learning, by
Kazuki Irie and 2 other authors
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    Abstract:General-purpose learning systems should improve
    themselves in open-ended fashion in ever-changing environments.
    Conventional learning algorithms for neural networks, however,
    suffer from catastrophic forgetting (CF) -- previously acquired
    skills are forgotten when a new task is learned. Instead of
    hand-crafting new algorithms for avoiding CF, we propose
    Automated Continual Learning (ACL) to train self-referential
    neural networks to meta-learn their own in-context continual
    (meta-)learning algorithms. ACL encodes all desiderata -- good
    performance on both old and new tasks -- into its meta-learning
    objectives. Our experiments demonstrate that ACL effectively
    solves "in-context catastrophic forgetting"; our ACL-learned
    algorithms outperform hand-crafted ones, e.g., on the Split-MNIST
    benchmark in the replay-free setting, and enables continual
    learning of diverse tasks consisting of multiple few-shot and
    standard image classification datasets.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2312.00276 [cs.LG]
          (or arXiv:2312.00276v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2312.00276
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Kazuki Irie [view email]
[v1] Fri, 1 Dec 2023 01:25:04 UTC (314 KB)
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