Gated Recurrent Units Viewed Through The Lens Of Continuous Time Dynamical Systems

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Donate! Computer Science > Machine Learning arXiv:1906.01005 (cs) [Submitted on 3 Jun 2019 (v1), last revised 29 Jul 2021 (this version, v2)] Title:Gated recurrent units viewed through the lens of continuous time dynamical systems Authors:Ian D. Jordan, Piotr Aleksander Sokol, Il Memming Park View a PDF of the paper titled Gated recurrent units viewed through the lens of continuous time dynamical systems, by Ian D. Jordan and 2 other authors View PDF

Abstract:Gated recurrent units (GRUs) are specialized memory elements for building recurrent neural networks. Despite their incredible success on various tasks, including extracting dynamics underlying neural data, little is understood about the specific dynamics representable in a GRU network. As a result, it is both difficult to know a priori how successful a GRU network will perform on a given task, and also their capacity to mimic the underlying behavior of their biological counterparts. Using a continuous time analysis, we gain intuition on the inner workings of GRU networks. We restrict our presentation to low dimensions, allowing for a comprehensive visualization. We found a surprisingly rich repertoire of dynamical features that includes stable limit cycles (nonlinear oscillations), multi-stable dynamics with various topologies, and homoclinic bifurcations. At the same time we were unable to train GRU networks to produce continuous attractors, which are hypothesized to exist in biological neural networks. We contextualize the usefulness of different kinds of observed dynamics and support our claims experimentally.

Subjects:	Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as:	arXiv:1906.01005 [cs.LG]
(or arXiv:1906.01005v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.1906.01005 Focus to learn more arXiv-issued DOI via DataCite
Journal reference:	Frontiers in Computational Neuroscience, 2021
Related DOI:	https://doi.org/10.3389/fncom.2021.678158 Focus to learn more DOI(s) linking to related resources

Submission history

From: Ian Jordan [view email] [v1] Mon, 3 Jun 2019 18:13:32 UTC (8,682 KB) [v2] Thu, 29 Jul 2021 02:50:50 UTC (9,357 KB) Full-text links:

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