<  Back to the Polytechnique Montréal portal

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)

An external link is available for this item
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: 18 Apr 2023 15:02
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

Statistics

Stats are not available on this system.

Repository Staff Only

View Item View Item