publication . Preprint . 2015

Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series

Berglund, Mathias; Raiko, Tapani; Honkala, Mikko; Kärkkäinen, Leo; Vetek, Akos; Karhunen, Juha;
Open Access English
  • Published: 07 Apr 2015
Abstract
Bidirectional recurrent neural networks (RNN) are trained to predict both in the positive and negative time directions simultaneously. They have not been used commonly in unsupervised tasks, because a probabilistic interpretation of the model has been difficult. Recently, two different frameworks, GSN and NADE, provide a connection between reconstruction and probabilistic modeling, which makes the interpretation possible. As far as we know, neither GSN or NADE have been studied in the context of time series before. As an example of an unsupervised task, we study the problem of filling in gaps in high-dimensional time series with complex dynamics. Although unidir...
Subjects
free text keywords: Computer Science - Learning, Computer Science - Neural and Evolutionary Computing
Download from
27 references, page 1 of 2

[1] Bahdanau, D., Cho, K., and Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations (ICLR 2015).

[2] Baldi, P., Brunak, S., Frasconi, P., Soda, G., and Pollastri, G. (1999). Exploiting the past and the future in protein secondary structure prediction. Bioinformatics, 15(11), 937-946. [OpenAIRE]

[3] Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A., Bouchard, N., and Bengio, Y. (2012). Theano: new features and speed improvements. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop.

[4] Bayer, J. and Osendorfer, C. (2014). arXiv:1411.7610.

[5] Bengio, Y., Simard, P., and Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166. [OpenAIRE]

[6] Bengio, Y., Yao, L., Alain, G., and Vincent, P. (2013). Generalized denoising auto-encoders as generative models. In Advances in Neural Information Processing Systems, pages 899-907.

[7] Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y. (2010). Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy 2010). Oral Presentation. [OpenAIRE]

[8] Boulanger-Lewandowski, N., Bengio, Y., and Vincent, P. (2012). Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In Proceedings of the 29th International Conference on Machine Learning (ICML 2012), pages 1159-1166.

[9] Brakel, P., Stroobandt, D., and Schrauwen, B. (2013). Training energy-based models for time-series imputation. The Journal of Machine Learning Research, 14(1), 2771-2797.

[10] Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In International conference on artificial intelligence and statistics, pages 249-256.

[11] Goodfellow, I., Mirza, M., Courville, A., and Bengio, Y. (2013). Multi-prediction deep boltzmann machines. In Advances in Neural Information Processing Systems, pages 548-556.

[12] Graves, A., Liwicki, M., Ferna´ndez, S., Bertolami, R., Bunke, H., and Schmidhuber, J. (2009). A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855-868.

[13] Graves, A., Mohamed, A.-r., and Hinton, G. (2013). Speech recognition with deep recurrent neural networks. arXiv preprint arXiv:1303.5778.

[14] Haykin, S. (2009). Neural networks and learning machines, volume 3. Pearson Education.

[15] Hermans, M. and Schrauwen, B. (2013). Training and analysing deep recurrent neural networks. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 190-198. Curran Associates, Inc.

27 references, page 1 of 2
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue