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Bidirectional recurrent neural networks (RNN) are trained to predict both in the positive and negative time directions simultaneously.
Learning representations by back-propagating errors
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Learning long-term dependencies with gradient descent is difficult
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Long short-term memory
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Exploiting the past and the future in protein secondary structure prediction
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Missing values in nonlinear factor analysis
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A novel connectionist system for unconstrained handwriting recognition
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Theano: a CPU and GPU math expression compiler
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Understanding the difficulty of training deep feedforward neural networks
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Missing-feature reconstruction with a bounded nonlinear state-space model
Remes, U., Palomäki, K., Raiko, T., Honkela, A., and Kurimo, M. (2011) · 2011
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Generating text with recurrent neural networks
Sutskever, I., Martens, J., and Hinton, G. E. (2011) · 2011
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Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A., Bouchard, N., and Bengio, Y. (2012) · 2012
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Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription
Boulanger-Lewandowski, N., Bengio, Y., and Vincent, P. (2012) · 2012
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Generalized denoising auto-encoders as generative models
Training and analysing deep recurrent neural networks
Hermans, M. and Schrauwen, B. (2013) · 2013
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On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y. (2013) · 2013
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Learning stochastic recurrent networks
Bayer, J. and Osendorfer, C. (2014) · 2014
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A clockwork RNN
Koutník, J., Greff, K., Gomez, F., and Schmidhuber, J. (2014) · 2014
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First-pass large vocabulary continuous speech recognition using bi-directional recurrent dnns
Maas, A. L., Hannun, A. Y., Jurafsky, D., and Ng, A. Y. (2014) · 2014
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Training energy-based models for time-series imputation
Brakel, P., Stroobandt, D., and Schrauwen, B. (2013) · 2013
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Multi-prediction deep boltzmann machines
Goodfellow, I., Mirza, M., Courville, A., and Bengio, Y. (2013) · 2013
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Speech recognition with deep recurrent neural networks
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Mikolov, T., Joulin, A., Chopra, S., Mathieu, M., and Ranzato, M. (2014) · 2014
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A deep and tractable density estimator
Uria, B., Murray, I., and Larochelle, H. (2014) · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y. (2015) · 2015
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Techniques for learning binary stochastic feedforward neural networks
Raiko, T., Berglund, M., Alain, G., and Dinh, L. (2015) · 2015
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