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Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train.
A statistical derivation of the significant-digit law
Hill, Theodore P · 1995
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The use of recurrent neural networks in continuous speech recognition
Robinson, Tony, Hochberg, Mike, and Renals, Steve · 1996
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Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
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Optimization and applications of echo state networks with leaky-integrator neurons
Jaeger, Herbert, Lukoševičius, Mantas, Popovici, Dan, and Siewert, Udo · 2007
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Curriculum learning
Bengio, Yoshua, Louradour, Jérôme, Collobert, Ronan, and Weston, Jason · 2009
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Self-paced learning for latent variable models
Kumar, M Pawan, Packer, Benjamin, and Koller, Daphne · 2010
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Deep learning via hessian-free optimization
Martens, James · 2010
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Recurrent neural network based language model
Mikolov, Tomas, Karafiát, Martin, Burget, Lukas, Cernockỳ, Jan, and Khudanpur, Sanjeev · 2010
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Learning the easy things first: Self-paced visual category discovery
Lee, Yong Jae and Grauman, Kristen · 2011
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Statistical language models based on neural networks
Mikolov, Tomáš · 2012
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Advances in optimizing recurrent networks
Bengio, Yoshua, Boulanger-Lewandowski, Nicolas, and Pascanu, Razvan · 2013
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Can recursive neural tensor networks learn logical reasoning?
Bowman, Samuel R · 2013
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Speech recognition with deep recurrent neural networks
Graves, Alex, Mohamed, Abdel-rahman, and Hinton, Geoffrey · 2013
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Training Recurrent Neural Networks
Sutskever, Ilya · 2013
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Recursive neural networks for learning logical semantics
Bowman, Samuel R, Potts, Christopher, and Manning, Christopher D · 2014
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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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Koutník, Jan, Greff, Klaus, Gomez, Faustino, and Schmidhuber, Jürgen · 2014
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Structured generative models of natural source code
Maddison, Chris J and Tarlow, Daniel · 2014
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Pascanu, Razvan, Gulcehre, Caglar, Cho, Kyunghyun, and Bengio, Yoshua · 2013
Cited alongside, same era.
Dropout improves recurrent neural networks for handwriting recognition
Pham, Vu, Kermorvant, Christopher, and Louradour, Jérôme · 2013
Cited alongside, same era.
Learning to discover efficient mathematical identities
Zaremba, Wojciech, Kurach, Karol, and Fergus, Rob
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Recurrent neural network regularization
Zaremba, Wojciech, Sutskever, Ilya, and Vinyals, Oriol
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Building program vector representations for deep learning
Mou, Lili, Li, Ge, Liu, Yuxuan, Peng, Hao, Jin, Zhi, Xu, Yan, and Zhang, Lu · 2014
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Sequence to sequence learning with neural networks
Sutskever, Ilya, Vinyals, Oriol, and Le, Quoc V · 2014
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