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Computer Science > Machine Learning

arXiv:2410.01201 (cs)
[Submitted on 2 Oct 2024]

Title:Were RNNs All We Needed?

Authors:Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio,
Hossein Hajimirsadegh
View a PDF of the paper titled Were RNNs All We Needed?, by Leo Feng
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    Abstract:The scalability limitations of Transformers regarding
    sequence length have renewed interest in recurrent sequence
    models that are parallelizable during training. As a result, many
    novel recurrent architectures, such as S4, Mamba, and Aaren, have
    been proposed that achieve comparable performance. In this work,
    we revisit traditional recurrent neural networks (RNNs) from over
    a decade ago: LSTMs (1997) and GRUs (2014). While these models
    were slow due to requiring to backpropagate through time (BPTT),
    we show that by removing their hidden state dependencies from
    their input, forget, and update gates, LSTMs and GRUs no longer
    need to BPTT and can be efficiently trained in parallel. Building
    on this, we introduce minimal versions (minLSTMs and minGRUs)
    that (1) use significantly fewer parameters than their
    traditional counterparts and (2) are fully parallelizable during
    training (175x faster for a sequence of length 512). Lastly, we
    show that these stripped-down versions of decade-old RNNs match
    the empirical performance of recent sequence models.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2410.01201 [cs.LG]
          (or arXiv:2410.01201v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2410.01201
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Leo Feng [view email]
[v1] Wed, 2 Oct 2024 03:06:49 UTC (292 KB)
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