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Beyond exploding an...
Beyond exploding and vanishing gradients : analysing RNN training using attractors and smoothness
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- Ribeiro, Antonio H. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil
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- Tiels, Koen (författare)
- Eindhoven Univ Technol, Eindhoven, Netherlands
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- Aguirre, Luis A. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil
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- Schön, Thomas B., Professor, 1977- (författare)
- Uppsala universitet,Avdelningen för systemteknik,Artificiell intelligens
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(creator_code:org_t)
- 2020
- 2020
- Engelska.
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Ingår i: Proceedings of the 23<sup>rd</sup> International Conference on Artificial Intelligence and Statistics (AISTATS). ; , s. 2370-2380
- Relaterad länk:
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http://proceedings.m...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- The exploding and vanishing gradient problem has been the major conceptual principle behind most architecture and training improvements in recurrent neural networks (RNNs) during the last decade. In this paper, we argue that this principle, while powerful, might need some refinement to explain recent developments. We refine the concept of exploding gradients by reformulating the problem in terms of the cost function smoothness, which gives insight into higher-order derivatives and the existence of regions with many close local minima. We also clarify the distinction between vanishing gradients and the need for the RNN to learn attractors to fully use its expressive power. Through the lens of these refinements, we shed new light on recent developments in the RNN field, namely stable RNN and unitary (or orthogonal) RNNs.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)