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Many machine learning tasks can be expressed as the transformation---or \emph{transduction}---of input sequences into output sequences: speech recognition, machine translation, protein secondary structure prediction and text-to-speech to name but a few.
Speaker-independent phone recognition using hidden markov models
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LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Gers, F · 2001
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Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-term Dependencies
Hochreiter, S., Bengio, Y., Frasconi, P., and Schmidhuber, J · 2001
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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J. D., McCallum, A., and Pereira, F. C. N · 2001
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Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A. and Schmidhuber, J · 2005
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Rejection strategies for offline handwritten text line recognition
Bertolami, R, Zimmermann, M, and Bunke, H · 2006
Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks
Graves, A., Fernández, S., Gomez, F., and Schmidhuber, J · 2006
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Offline handwriting recognition with multidimensional recurrent neural networks
Graves, A. and Schmidhuber, J · 2008
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Unconstrained Online Handwriting Recognition with Recurrent Neural Networks
Graves, A., Fernández, S., Liwicki, M., Bunke, H., and Schmidhuber, J · 2008
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Phone recognition with the mean-covariance restricted boltzmann machine
Dahl, G., Ranzato, M., Mohamed, A., and Hinton, G · 2010
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Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Cernocky, J., and Khudanpur, S · 2010
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Generating text with recurrent neural networks
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Cited alongside, same era.
Sutskever, I., Martens, J., and Hinton, G · 2011
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