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We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units.
Byblos: The bbn continuous speech recognition system
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Bourlard, H. and Morgan, N · 1993
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Marcus, Mitchell P, Marcinkiewicz, Mary Ann, and Santorini, Beatrice · 1993
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The use of recurrent neural networks in continuous speech recognition
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Long short-term memory
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Optimization and applications of echo state networks with leaky-integrator neurons
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A novel connectionist system for unconstrained handwriting recognition
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Context dependent recurrent neural network language model
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Speech recognition with deep recurrent neural networks
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Recurrent continuous translation models
Regularization of neural networks using dropconnect
Wan, Li, Zeiler, Matthew, Zhang, Sixin, Cun, Yann L, and Fergus, Rob · 2013
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Fast dropout training
Wang, Sida and Manning, Christopher · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, Kyunghyun, van Merrienboer, Bart, Gulcehre, Caglar, Bougares, Fethi, Schwenk, Holger, and Bengio, Yoshua · 2014
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Fast and robust neural network joint models for statistical machine translation
Devlin, J., Zbib, R., Huang, Z., Lamar, T., Schwartz, R., and Makhoul, J · 2014
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Exploiting similarities among languages for machine translation
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Regularization and nonlinearities for neural language models: when are they needed?
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Dropout improves recurrent neural networks for handwriting recognition
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Cheng, Wei-Chen, Kok, Stanley, Pham, Hoai Vu, Chieu, Hai Leong, and Chai, Kian Ming A
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Sequence discriminative distributed training of long short-term memory recurrent neural networks
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University le mans, 2014
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Going deeper with convolutions
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Show and tell: A neural image caption generator
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