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Computer Science > Neural and Evolutionary Computing

arXiv:2408.11946 (cs)
[Submitted on 19 Jul 2024]

Title:A Technical Note on the Architectural Effects on Maximum
Dependency Lengths of Recurrent Neural Networks

Authors:Jonathan S. Kent, Michael M. Murray
View a PDF of the paper titled A Technical Note on the Architectural
Effects on Maximum Dependency Lengths of Recurrent Neural Networks,
by Jonathan S. Kent and 1 other authors
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    Abstract:This work proposes a methodology for determining the
    maximum dependency length of a recurrent neural network (RNN),
    and then studies the effects of architectural changes, including
    the number and neuron count of layers, on the maximum dependency
    lengths of traditional RNN, gated recurrent unit (GRU), and
    long-short term memory (LSTM) models.

Comments:    13 pages, 12 figures
Subjects:    Neural and Evolutionary Computing (cs.NE)
ACM classes: I.2.6
Cite as:     arXiv:2408.11946 [cs.NE]
             (or arXiv:2408.11946v1 [cs.NE] for this version)
             https://doi.org/10.48550/arXiv.2408.11946
             Focus to learn more
             arXiv-issued DOI via DataCite

Submission history

From: Jonathan Kent [view email]
[v1] Fri, 19 Jul 2024 23:00:38 UTC (588 KB)
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