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Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling.
Advances in neural information processing systems 1
Stephen José Hanson and Lorien Pratt · 1989
Earlier work this paper cites.
Optimal brain damage
Yann LeCun, John S Denker, Sara A Solla, Richard E Howard, and Lawrence D Jackel · 1989
Earlier work this paper cites.
Optimal brain surgeon and general network pruning
Babak Hassibi, David G Stork, and Gregory J Wolff · 1993
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber · 2006
Earlier work this paper cites.
Tree-guided group lasso for multi-task regression with structured sparsity
Seyoung Kim and Eric P Xing · 2010
Earlier work this paper cites.
Improving the speed of neural networks on cpus
Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao · 2011
Earlier work this paper cites.
Exploiting sparseness in deep neural networks for large vocabulary speech recognition
Dong Yu, Frank Seide, Gang Li, and Li Deng · 2012
Earlier work this paper cites.
Predicting parameters in deep learning
Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando de Freitas · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
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Cited alongside, same era.
Song Han, Huizi Mao, and William J Dally · 2015
Cited alongside, same era.
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Cited alongside, same era.
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Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, JingDong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, et al · 2016
Cited alongside, same era.
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