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Background

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. link

Theory

Spectral properties

Other

  • Nikolova, M. (2014). Relationship between the optimal solutions of least squares regularized with L0-norm and constrained by k-sparsity. Applied and Computational Harmonic Analysis, 1(1), 0–24. http://doi.org/10.1016/j.acha.2015.10.010

Applications

  • Chung, Y.-A., Wu, C.-C., Shen, C.-H., & Lee, H.-Y. (2016). Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Recurrent Neural Networks. Retrieved from http://arxiv.org/abs/1603.00982
  • Hammerla, N. Y., Halloran, S., & Ploetz, T. (2016). Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables. Ijcai. Retrieved from http://arxiv.org/abs/1604.08880
  • Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzell, R. (2015). Learning to Diagnose with LSTM Recurrent Neural Networks, 1–18. Retrieved from http://arxiv.org/abs/1511.03677
  • Malhotra, P., Vig, L., Shroff, G., & Agarwal, P. (2015). Long Short Term Memory Networks for Anomaly Detection in Time Series. European Symposium on Artificial Neural Networks, (April), 22–24.
  • Tagliaferri, R., Ciaramella, A., Barone, F., & Milano, L. (1999). Neural Networks for Spectral Analysis of Unevenly Sampled Data. Retrieved from http://arxiv.org/abs/astro-ph/9906181
  • Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2014). Exploiting Multi-Channels Deep Convolutional Neural Networks for Multivariate Time Series Classification, 8–10.
  • Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2014). Time series classification using multi-channels deep convolutional neural networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8485 LNCS, 298–310. http://doi.org/10.1007/978-3-319-08010-9-33
  • Batres-estrada, G. (2015). Deep Learning for Multivariate Financial Time Series.
  • Björklund, J., Eksvärd, K., & Schaffer, C. (2013). A review of unsupervised feature learning and deep learning for time-series modeling, 18(April), 1–4.
  • Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., … Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition. Ieee Signal Processing Magazine, (November), 82–97. http://doi.org/10.1109/MSP.2012.2205597
  • Humphrey, E. J., Bello, J. P., & Lecun, Y. (2013). Feature learning and deep architectures: New directions for music informatics. Journal of Intelligent Information Systems, 41(3), 461–481. http://doi.org/10.1007/s10844-013-0248-5
  • Hüsken, M., & Stagge, P. (2003). Recurrent neural networks for time series classification. Neurocomputing, 50(C), 223–235. http://doi.org/10.1016/S0925-2312(01)00706-8
  • Lee, H., Pham, P., Largman, Y., & Ng, A. (2009). Unsupervised feature learning for audio classification using convolutional deep belief networks. Nips, 1–9. http://doi.org/10.1145/1553374.1553453
  • Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015). Unsupervised Learning of Video Representations using LSTMs. Bmvc2015, 2009. http://doi.org/citeulike-article-id:13519737
  • Walker, I. (2015). Deep Convolutional Neural Networks for Brain Computer Interface using Motor Imagery.
  • Wulsin, D. F., Gupta, J. R., Mani, R., Blanco, J. A., & Litt, B. (2012). Modeling EEG Waveforms with Semi-Supervised Deep Belief Nets: Fast Classification and Anomaly Measurement, 8(3), 1–28. http://doi.org/10.1088/1741-2560/8/3/036015.Modeling