A Novel Inference of a Restricted Boltzmann Machine

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2014, Pages 1526-1531

  Tanaka, Masayuki ^a   Okutomi, Masatoshi ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP NEURAL NETWORKS; NEURAL NETWORKS; PATTERN RECOGNITION;

BINARY VARIABLES; CLOSED FORM APPROXIMATIONS; COMPUTATIONAL COSTS; CONVENTIONAL ALGORITHMS; EXPERIMENTAL COMPARISON; REAL VALUES; RESTRICTED BOLTZMANN MACHINE;

INFERENCE ENGINES;

EID: 84919904236     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2014.271     Document Type: Conference Paper

References (16)

1. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al., "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups," IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012.
2. H. Lee, C. Ekanadham, and A. Ng, "Sparse deep belief net model for visual area v2," in Advances in neural information processing systems, 2007, pp. 873-880.
3. L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus, "Regularization of neural networks using dropconnect," in International Conference on Machine Learning (ICML), 2013, pp. 1058-1066.
4. S. Wang and C. Manning, "Fast dropout training," in International Conference on Machine Learning (ICML), 2013, pp. 118-126.
5. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, 2006.
6. K. Cho, A. Ilin, and T. Raiko, "Improved learning of gaussian-bernoulli restricted boltzmann machines," in Artificial Neural Networks and Machine Learning (ICANN). Springer, 2011, pp. 10-17.
7. M. Ranzato, V. Mnih, J. Susskind, and G. Hinton, "Modeling natural images using gated mrfs," IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), vol. 35, no. 9, pp. 2206-2222, 2013.
8. G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural computation, vol. 18, no. 7, pp. 1527-1554, 2006.
9. G. E. Hinton, "Training products of experts by minimizing contrastive divergence," Neural computation, vol. 14, no. 8, pp. 1771-1800, 2002.
10. T. Tieleman and G. Hinton, "Using fast weights to improve persistent contrastive divergence," in International Conference on Machine Learning (ICML). ACM, 2009, pp. 1033-1040.
11. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Cognitive modeling, vol. 1, p. 213, 2002.
12. D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, "Why does unsupervised pre-training help deep learning?" The Journal of Machine Learning Research, vol. 11, pp. 625-660, 2010.
13. G. Hinton, "A practical guide to training restricted boltzmann machines," UTML TR 2010-003, 2010.
14. A. Krizhevsky, "Learning multiple layers of featuresfrom tiny images," in Master's thesis, Dept. of Comp. Sci., University of Toronto., IEEE, 2009.
15. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
16. T. A., F. R., and F. W.T., "80 million tiny images: A large data set for nonparametric object and scene recognition," IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), vol. 30, no. 11, pp. 1958-1970, 2008