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Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task.
A systemic study of monetary systems
Eduardo R Caianiello, Gaetano Scarpetta, and Giovanna Simoncelli · 1982
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Building a large annotated corpus of english: The penn treebank
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini · 1993
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Hierarchical recurrent neural networks for long-term dependencies
Salah El Hihi and Yoshua Bengio · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Short term memory in echo state networks
Herbert Jaeger · 2001
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A brief survey on sequence classification
Zhengzheng Xing, Jian Pei, and Eamonn Keogh · 2010
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
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English multi-speaker corpus for cstr voice cloning toolkit
Junichi Yamagishi · 2012
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
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Jan Koutnik, Klaus Greff, Faustino Gomez, and Juergen Schmidhuber · 2014
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A simple way to initialize recurrent networks of rectified linear units
Quoc V Le, Navdeep Jaitly, and Geoffrey E Hinton · 2015
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Learning the speech front-end with raw waveform cldnns
Tara N Sainath, Ron J Weiss, Andrew Senior, Kevin W Wilson, and Oriol Vinyals · 2015
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
David Ha, Andrew Dai, and Quoc V Le · 2016
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Zoneout: Regularizing rnns by randomly preserving hidden activations
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron Courville, et al · 2016
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Phased LSTM: accelerating recurrent network training for long or event-based sequences
Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu · 2016
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Recurrent dropout without memory loss
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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Unitary evolution recurrent neural networks
Martin Arjovsky, Amar Shah, and Yoshua Bengio · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Hierarchical multiscale recurrent neural networks
Junyoung Chung, Sungjin Ahn, and Yoshua Bengio · 2016
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Tim Cooijmans, Nicolas Ballas, César Laurent, Çağlar Gülçehre, and Aaron Courville · 2016
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Full-capacity unitary recurrent neural networks
Scott Wisdom, Thomas Powers, John Hershey, Jonathan Le Roux, and Les Atlas · 2016
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Architectural complexity measures of recurrent neural networks
Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan R Salakhutdinov, and Yoshua Bengio · 2016
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Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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Adams W Yu, Hongrae Lee, and Quoc V Le · 2017
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Fisher Yu, Vladlen Koltun, and Thomas Funkhouser · 2017
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