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Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems.
Timit acousic-phonetic coninuous speech corpus ldc93sl
Garofolo, J. S., Lamel, L. F., Fisher, W. M., Fiscus, J. G., Pallett, D. S., Dahlgren, N. L., and Zue, V. (1993) · 1993
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Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., and Frasconi, P. (1994) · 1994
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Long short-term memory
Hochreiter, S. and Schmidhuber, J. (1997) · 1997
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Is there a small skew cayley transform with zero diagonal?
Kahan, W. (2006) · 2006
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An introduction to complex differential and complex differentiability
Hunger, R. (2007) · 2007
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The complex gradient operator and the cr-calculus
Kreutz-Delgado, K. (2009) · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
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MNIST handwritten digit database
LeCun, Y. and Cortes, C. (2010) · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E. (2010) · 2010
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
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A simple way to initialize recurrent networks of rectified linear units
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Unitary evolution recurrent neural networks
Arjovsky, M., Shah, A., and Bengio, Y. (2016) · 2016
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Jing, L., Gülçehre, C., Peurifoy, J., Shen, Y., Tegmark, M., Soljačić, M., and Bengio, Y. (2017) · 2017
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Efficient orthogonal parametrisation of recurrent neural networks using householder reflections
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On orthogonality and learning recurrent networks with long term dependencies
Vorontsov, E., Trabelsi, C., Kadoury, S., and Pal, C. (2017) · 2017
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Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Helfrich, K., Willmott, D., and Ye, Q. (2018) · 2018
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Trabelsi, C., Bilaniuk, O., Zhang, Y., Serdyuk, D., Subramanian, S., Santos, J. F., Mehri, S., Rostamzadeh, N., Bengio, Y., and Pal, C. (2018) · 2018
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Jing, L., Shen, Y., Dubcek, T., Peurifoy, J., Skirlo, S. A., Tegmark, M., and Soljacic, M. (2016) · 2016
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Full-capacity unitary recurrent neural networks
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