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We introduce recurrent additive networks (RANs), a new gated RNN which is distinguished by the use of purely additive latent state updates.
Finding structure in time
Jeffrey L. Elman · 1990
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Building a large annotated corpus of english: The penn treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz · 1993
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Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Y. Simard, and Paolo Frasconi · 1994
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Long Short-term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Recurrent nets that time and count
Felix A. Gers and Jürgen Schmidhuber · 2000
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, and Phillipp Koehn · 2014
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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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An empirical exploration of recurrent network architectures
Rafal Józefowicz, Wojciech Zaremba, and Ilya Sutskever · 2015
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Long short-term memory-networks for machine reading
Jianpeng Cheng, Li Dong, and Mirella Lapata · 2016
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Lstm: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber · 2016
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Exploring the limits of language modeling
Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
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Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg · 2016
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A decomposable attention model for natural language inference
A simple but tough-to-beat baseline for sentence embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma · 2017
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Hierarchical multiscale recurrent neural networks
Junyoung Chung, Sungjin Ahn, and Yoshua Bengio · 2017
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Recurrent batch normalization
Tim Cooijmans, Nicolas Ballas, César Laurent, and Aaron C. Courville · 2017
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Deep semantic role labeling: What works and what’s next
Luheng He, Kenton Lee, Mike Lewis, and Luke Zettlemoyer · 2017
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Multiplicative LSTM for sequence modelling
Ben Krause, Iain Murray, Steve Renals, and Liang Lu · 2017
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Deriving neural architectures from sequence and graph kernels
Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2017
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Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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On multiplicative integration with recurrent neural networks
Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, and Ruslan Salakhutdinov · 2016
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Architectural complexity measures of recurrent neural networks
Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, and Yoshua Bengio · 2016
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Recurrent highway networks
Julian G. Zilly, Rupesh Kumar Srivastava, Jan Koutník, and Jürgen Schmidhuber · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
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