Conditional Restricted Boltzmann Machines for structured output prediction

Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

Volumn , Issue , 2011, Pages 514-522

  Mnih, Volodymyr ^a   Larochelle, Hugo ^b   Hinton, Geoffrey E ^a  

^a UNIVERSITY OF TORONTO   (Canada)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COLLABORATIVE FILTERING; FORECASTING; IMAGE DENOISING;

ARBITRARY STRUCTURES; CONDITIONAL MODELS; CONDITIONAL RESTRICTED BOLTZMANN MACHINES; CONTRASTIVE DIVERGENCE; MULTI LABEL CLASSIFICATION; PROBABILISTIC MODELS; SPACE VARIABILITY; STRUCTURED OUTPUT PREDICTION;

LEARNING ALGORITHMS;

EID: 80053146489     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for boltzmann machines. Cognitive Science, 9, 147-169.
2. Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37, 1757-1771. (Pubitemid 38804465)
3. Desjardins, G., Courville, A., Bengio, Y., Vincent, P., & Delalleau, O. (2010). Multiscale conditional random fields for image labeling. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (pp. 145-152).
4. Elisseeff, A., & Weston, J. (2002). A kernel method for multi-labelled classification. Advances in Neural Information Processing Systems 14 (NIPS) (pp. 681-687). MIT Press.
5. Gehler, P. V., Holub, A. D., & Welling, M. (2006). The rate adapting poisson model for information retrieval and object recognition. Proceedings of the Twenty-three International Conference on Machine Learning (ICML) (pp. 337-344). ACM.
6. Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14, 1771-1800.
7. Hyvärinen, A. (2007). Some extensions of score matching. Comput. Stat. Data Anal., 51, 2499-2512. (Pubitemid 44751259)
8. Larochelle, H., & Bengio, Y. (2008). Classification using discriminative restricted boltzmann machines. Proceedings of the 25th International Conference on Machine Learning (ICML) (pp. 536-543). ACM.
9. LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., & Huang, F. (2006). A tutorial on energy-based learning. Predicting Structured Data. MIT Press.
10. Mandel, M., Pascanu, R., Larochelle, H., & Bengio, Y. (2011). Autotagging music with conditional restricted Boltzmann machines. ArXiv e-prints.
11. Mandel, M. I., Eck, D., & Bengio, Y. (2010). Learning tags that vary within a song. Proceedings of the 11th International Conference on Music Information Retrieval (ISMIR) (pp. 399-404).
12. Mandel, M. I., & Ellis, D. P. W. (2008). A web-based game for collecting music metadata. Journal New of Music Research, 37, 151-165.
13. Memisevic, R., & Hinton, G. E. (2010). Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural Computation, 22, 1473-1492.
14. Murphy, K. P., Weiss, Y., & Jordan, M. I. (1999). Loopy belief propagation for approximate inference: An empirical study. Proceedings of the 15th Conference in Uncertainty in Artificial Intelligence (UAI) (pp. 467-475).
15. Osindero, S., & Hinton, G. E. (2008). Modeling image patches with a directed hierarchy of markov random field. Advances in Neural Information Processing Systems 20 (NIPS) (pp. 1121-1128). MIT Press.
16. Salakhutdinov, R. (2010). Learning deep boltzmann machines using adaptive mcmc. Proceedings of the 27th International Conference on Machine Learning (ICML).
17. Salakhutdinov, R., & Hinton, G. E. (2007). Semantic Hashing. SIGIR workshop on Information Retrieval and applications of Graphical Models.
18. Salakhutdinov, R., & Hinton, G. E. (2009). Replicated softmax: an undirected topic model. Advances in Neural Information Processing Systems 22 (NIPS) (pp. 1607-1614). MIT Press.
19. Salakhutdinov, R., Mnih, A., & Hinton, G. E. (2007). Restricted Boltzmann machines for collaborative filtering. Proceedings of the 24th International Conference on Machine Learning (ICML) (pp. 791-798). ACM.
20. Sutskever, I., & Tieleman, T. (2010). On the convergence properties of Contrastive Divergence. Proc. Conference on AI and Statistics (AI-Stats).
21. Taylor, G. W., & Hinton, G. E. (2009). Factored conditional restricted boltzmann machines for modeling motion style. Proceedings of the 26th International Conference on Machine Learning (ICML) (p. 129). ACM.
22. Tieleman, T. (2008). Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient. Proceedings of the 25th international conference on Machine learning (ICML) (pp. 1064- 1071). ACM.
23. Tieleman, T., & Hinton, G. (2009). Using FastWeights to Improve Persistent Contrastive Divergence. Proceedings of the 26th International Conference on Machine Learning (ICML) (pp. 1033-1040). ACM.
24. Vickrey, D., Lin, C. C.-Y., & Koller, D. (2010). Nonlocal contrastive objectives. International Conference on Machine Learning (pp. 1103-1110).
25. Weiss, Y., Torralba, A., & Fergus, R. (2009). Spectral hashing. In D. Koller, D. Schuurmans, Y. Bengio and L. Bottou (Eds.), Advances in neural information processing systems 21 (NIPS), 1753-1760. MIT Press.