Preserving activations in recurrent neural networks based on surprisal-Reference-Cited by-同舟云学术

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Preserving activations in recurrent neural networks based on surprisal

Published:2019-05 Issue: Volume:342 Page:75-82
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Alpay Tayfun,Abawi Fares,Wermter Stefan

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference23 articles.

1. Long short-term memory;Hochreiter;Neural Comput. (NC),1997

2. Empirical evaluation of gated recurrent neural networks on sequence modeling;Chung,2014

3. LSTM: a search space odyssey;Greff;IEEE Trans. Neural Netw. Learn. Syst.,2017

4. Event-based analysis of video;Zelnik-Manor,2001

5. Estimating or propagating gradients through stochastic neurons for conditional computation;Bengio;Comput. Res. Repos. (CoRR),2013

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