Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Published: 11 Dec 2014
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- Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
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D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. Technical report, arXiv preprint arXiv:1409.0473, 2014.
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio. Theano: new features and speed improvements. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012.
Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2):157-166, 1994. [OpenAIRE]
Y. Bengio, N. Boulanger-Lewandowski, and R. Pascanu. Advances in optimizing recurrent networks. In Proc. ICASSP 38, 2013.
J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13(1):281-305, 2012.
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. WardeFarley, and Y. Bengio. Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010. Oral Presentation. [OpenAIRE]
N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent. Modeling temporal dependencies in highdimensional sequences: Application to polyphonic music generation and transcription. In Proceedings of the Twenty-nine International Conference on Machine Learning (ICML'12). ACM, 2012. URL http://icml.cc/discuss/2012/590.html.
K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259, 2014.
I. J. Goodfellow, D. Warde-Farley, P. Lamblin, V. Dumoulin, M. Mirza, R. Pascanu, J. Bergstra, F. Bastien, and Y. Bengio. Pylearn2: a machine learning research library. arXiv preprint arXiv:1308.4214, 2013.
A. Graves. Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence. Springer, 2012.
A. Graves. Practical variational inference for neural networks. In Advances in Neural Information Processing Systems, pages 2348-2356, 2011.
A. Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.
A. Graves, A.-r. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. In ICASSP'2013, pages 6645-6649. IEEE, 2013.
C. Gulcehre, K. Cho, R. Pascanu, and Y. Bengio. Learned-norm pooling for deep feedforward and recurrent neural networks. In Machine Learning and Knowledge Discovery in Databases, pages 530-546. Springer, 2014.
G. Hinton. Neural networks for machine learning. Coursera, video lectures, 2012.
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- Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
- 1
- 2
D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. Technical report, arXiv preprint arXiv:1409.0473, 2014.
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio. Theano: new features and speed improvements. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012.
Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2):157-166, 1994. [OpenAIRE]
Y. Bengio, N. Boulanger-Lewandowski, and R. Pascanu. Advances in optimizing recurrent networks. In Proc. ICASSP 38, 2013.
J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13(1):281-305, 2012.
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. WardeFarley, and Y. Bengio. Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010. Oral Presentation. [OpenAIRE]
N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent. Modeling temporal dependencies in highdimensional sequences: Application to polyphonic music generation and transcription. In Proceedings of the Twenty-nine International Conference on Machine Learning (ICML'12). ACM, 2012. URL http://icml.cc/discuss/2012/590.html.
K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259, 2014.
I. J. Goodfellow, D. Warde-Farley, P. Lamblin, V. Dumoulin, M. Mirza, R. Pascanu, J. Bergstra, F. Bastien, and Y. Bengio. Pylearn2: a machine learning research library. arXiv preprint arXiv:1308.4214, 2013.
A. Graves. Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence. Springer, 2012.
A. Graves. Practical variational inference for neural networks. In Advances in Neural Information Processing Systems, pages 2348-2356, 2011.
A. Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.
A. Graves, A.-r. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. In ICASSP'2013, pages 6645-6649. IEEE, 2013.
C. Gulcehre, K. Cho, R. Pascanu, and Y. Bengio. Learned-norm pooling for deep feedforward and recurrent neural networks. In Machine Learning and Knowledge Discovery in Databases, pages 530-546. Springer, 2014.
G. Hinton. Neural networks for machine learning. Coursera, video lectures, 2012.
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