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Recurrent neural networks (RNNs) are important class of architectures among neural networks useful for language modeling and sequential prediction.
Acceleration of stochastic approximation by averaging
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Long short-term memory
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Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernockỳ, and Sanjeev Khudanpur · 2010
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann L Cun, and Rob Fergus · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
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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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Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan · 2014
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Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, and Tapani Raiko · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio · 2015
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Batch normalized recurrent neural networks
César Laurent, Gabriel Pereyra, Philémon Brakel, Ying Zhang, and Yoshua Bengio · 2016
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Dropout with expectation-linear regularization
Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, and Eduard Hovy · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Using the output embedding to improve language models
Ofir Press and Lior Wolf · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Tim Cooijmans, Nicolas Ballas, César Laurent, and Aaron C. Courville · 2016
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A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal and Zoubin Ghahramani · 2016
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Tying word vectors and word classifiers: A loss framework for language modeling
Hakan Inan, Khashayar Khosravi, and Richard Socher · 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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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
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Regularizing and optimizing lstm language models
Stephen Merity, Nitish Shirish Keskar, and Richard Socher
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Revisiting activation regularization for language rnns
Stephen Merity, Bryan McCann, and Richard Socher
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Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, and Jürgen Schmidhuber · 2016
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Stochastic gradient descent as approximate bayesian inference
Stephan Mandt, Matthew D Hoffman, and David M Blei · 2017
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Twin networks: Matching the future for sequence generation
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