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Computer Science > Machine Learning

arXiv:1701.06538 (cs)
[Submitted on 23 Jan 2017]

Title:Outrageously Large Neural Networks: The Sparsely-Gated
Mixture-of-Experts Layer

Authors:Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy
Davis, Quoc Le, Geoffrey Hinton, Jeff Dean
Download a PDF of the paper titled Outrageously Large Neural
Networks: The Sparsely-Gated Mixture-of-Experts Layer, by Noam
Shazeer and 6 other authors
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    Abstract:The capacity of a neural network to absorb information
    is limited by its number of parameters. Conditional computation,
    where parts of the network are active on a per-example basis, has
    been proposed in theory as a way of dramatically increasing model
    capacity without a proportional increase in computation. In
    practice, however, there are significant algorithmic and
    performance challenges. In this work, we address these challenges
    and finally realize the promise of conditional computation,
    achieving greater than 1000x improvements in model capacity with
    only minor losses in computational efficiency on modern GPU
    clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer
    (MoE), consisting of up to thousands of feed-forward
    sub-networks. A trainable gating network determines a sparse
    combination of these experts to use for each example. We apply
    the MoE to the tasks of language modeling and machine
    translation, where model capacity is critical for absorbing the
    vast quantities of knowledge available in the training corpora.
    We present model architectures in which a MoE with up to 137
    billion parameters is applied convolutionally between stacked
    LSTM layers. On large language modeling and machine translation
    benchmarks, these models achieve significantly better results
    than state-of-the-art at lower computational cost.

          Machine Learning (cs.LG); Computation and Language (cs.CL);
Subjects: Neural and Evolutionary Computing (cs.NE); Machine Learning
          (stat.ML)
Cite as:  arXiv:1701.06538 [cs.LG]
          (or arXiv:1701.06538v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.1701.06538
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Noam Shazeer [view email]
[v1] Mon, 23 Jan 2017 18:10:00 UTC (294 KB)
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Noam Shazeer
Azalia Mirhoseini
Krzysztof Maziarz
Andy Davis
Quoc V. Le
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