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Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio and Guillaume Lajoie

Paper (2019)

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Department: Department of Mathematics and Industrial Engineering
PolyPublie URL: https://publications.polymtl.ca/45967/
Conference Title: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
Conference Location: Vancouver, B.-C.
Conference Date(s): 2019-12-08 - 2019-12-14
Editors: H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alche-Buc, E. Fox and R. Garnett
Publisher: Neural Information Processing Systems (Nips)
Official URL: https://papers.nips.cc/paper/9513-non-normal-recur...
Date Deposited: 18 Apr 2023 15:02
Last Modified: 25 Sep 2024 16:34
Cite in APA 7: Kerg, G., Goyette, K., Touzel, M. P., Gidel, G., Vorontsov, E., Bengio, Y., & Lajoie, G. (2019, December). Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics [Paper]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, B.-C.. https://papers.nips.cc/paper/9513-non-normal-recurrent-neural-network-nnrnn-learning-long-time-dependencies-while-improving-expressivity-with-transient-dynamics.pdf

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