References
- G. E. Hinton, "Learning multiple layers of representation," TRENDS Cognitive Sci., vol. 11, no. 10, pp. 428-434, 2007. https://doi.org/10.1016/j.tics.2007.09.004
- G. E. Dahl, M. Ranzato, A. Momamed, and G. E. Hinton, "Phone Recognition with the Mean-Covariance Restricted Boltzmann Machines," in Advances in Neural Information Processing Systems NIPS. Cambridge, MA, USA: MIT Press, 2010.
- G. Attardi, "a Deep Learning NLP pipeline" in Proceedings of NAACL-HLT 2015, pp. 109-115, May 31-June 5, 2015.
- A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, "Large-scale video classification with convolutional neural networks," in CVPR, 2014.
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
- L. Wan, M. Zeiler, S. Zhang, Y. LeCun, and R. Fergus, "Regularization of neural networks using dropconnect," Icml, no. 1, pp. 109-111, 2013.
- Al-Absi, A. A., and Kang, D.-K., "Long-read Alignment with Parallel MapReduce Cloud Platform," BioMed Research International, Vol. 2015, 2015.
- Lockhart, David J and Winzeler, Elizabeth A. Genomics, gene expression and DNA arrays. Nature, 405(6788): 827-836, 2000. https://doi.org/10.1038/35015701
- Clancy, S, "DNA transcription". Nature Education 1(1):41, 2008.
- G. E. Hinton, S. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets." Neural computation, vol. 18, no. 7, pp. 1527-54, 2006 https://doi.org/10.1162/neco.2006.18.7.1527
- A. Fischer, C. Igel, An Introduction to Restricted Boltzmann Machines," Progress in Pattern Recognition, Image Analysiss, Computer Vision, and Applications, vol. 7441, pp. 14-36, 2012.
- T. Tieleman, "Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient," Proceedings of the 25th International Conference on Machine Learning, vol. 307, p. 7, 2008.
- Mikolov, T., Chen, K., Greg, C. and Dean, J. (2013) Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.
- Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, "Greedy layer-wise training of deep networks," Advances in neural information processing systems, 19, p.153., 2007
- Splice Dataset, Machine Learning Repository, University of California, https://archive.ics.uci.edu/ml/machine-learning-databases/molecular-biology/splice-junction-gene-sequences/
- Promoter dataset, Machine Learning Repository, University of California, https://archive.ics.uci.edu/ml/machine-learning-databases/molecular-biology/promoter-gene-sequences/