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arXiv:1801.01078cs.NE

Recent Advances in Recurrent Neural Networks

Hojjat Salehinejad, Sharan Sankar, Joseph Barfett, Errol Colak, Shahrokh Valaee

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Abstract

Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.

arXiv comments
arXiv admin note: text overlap with arXiv:1602.04335
Journal reference
DOI
Subjects
cs.NENeural and Evolutionary Computing

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jan 2018 to Jan 2018. Found in 2 HN submissions from Jan 2018 to Feb 2019. Open submission discussions.

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  1. Neural Networks, Types, and Functional Programming (2015) 1 comment

    • This covers some of same topics plus the rapidly expanding #s of frameworks, SIMD /SIMT backends etc https://julialang.org/blog/2017/12/ml&pl 20 pg tutorial on why RNN's are tricky https://arxiv.org/abs/1801.01078

      gtaniView on HNParent context

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