Sparse Group Restricted Boltzmann Machines

Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011

Volumn , Issue , 2011, Pages 429-434

  Luo, Heng ^a   Shen, Ruimin ^a   Niu, Changyong ^b   Ullrich, Carsten ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b ZHENGZHOU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVATION PROBABILITIES; BOLTZMANN MACHINES; DEEP BOLTZMANN MACHINES; HIDDEN LAYERS; HIDDEN UNITS; OBSERVED DATA; REGULARISATION; REGULARIZER; RESTRICTED BOLTZMANN MACHINE; STATISTICAL DEPENDENCIES;

ARTIFICIAL INTELLIGENCE;

EID: 84899419802     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Bach, F. 2008. Consistency of the group Lasso and multiple kernel learning. The Journal of Machine Learning Research 9:1179-1225.
2. Bengio, Y. 2009. Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning 2(1):1-127.
3. Freund, Y., and Haussler, D. 1994. Unsupervised learning of distributions on binary vectors using two layer networks. 912-919.
4. Garrigues, P., and Olshausen, B. 2008. Learning horizontal connections in a sparse coding model of natural images. In Platt, J.; Koller, D.; Singer, Y.; and Roweis, S., eds., Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press.
5. Hinton, G., and Salakhutdinov, R. 2006. Reducing the dimensionality of data with neural networks. Science 313(5786):504.
6. Hinton, G. 2002. Training products of experts by minimizing contrastive divergence. Neural Computation 14(8):1771-1800.
7. Hoyer, P. 2004. Non-negative matrix factorization with sparseness constraints. The Journal of Machine Learning Research 5:1457-1469.
8. Larochelle, H., and Bengio, Y. 2008. Classification using discriminative restricted boltzmann machines. In Proceedings of the 25th international conference on Machine learning, 536-543. ACM.
9. Lee, H.; Grosse, R.; Ranganath, R.; and Ng, A. 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, 609-616. ACM.
10. Lee, H.; Ekanadham, C.; and Ng, A. 2008. Sparse deep belief net model for visual area v2. In Platt, J.; Koller, D.; Singer, Y.; and Roweis, S., eds., Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press.
11. Nair, V., and Hinton, G. E. 2009. Implicit Mixtures of Restricted Boltzmann Machines. In Koller, D.; Schuurmans, D.; Bengio, Y.; and Bottou, L., eds., Advances in Neural Information Processing Systems 21. 1145-1152.
12. Olshausen, B., et al. 1996. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607-609.
13. Salakhutdinov, R., and Hinton, G. 2009. Deep Boltzmann machines. In Proceedings of the International Conference on Artificial Intelligence and Statistics, volume 5, 448-455.
14. Salakhutdinov, R., and Larochelle, H. 2010. Efficient Learning of Deep Boltzmann Machines. In Proceedings of the International Conference on Artificial Intelligence and Statistics, volume 6.
15. Salakhutdinov, R., and Murray, I. 2008. On the quantitative analysis of deep belief networks. In Proceedings of the 25th international conference on Machine learning, 872-879. ACM.
16. Smolensky, P. 1986. Information processing in dynamical systems: foundations of harmony theory. In Parallel distributed processing: explorations in the microstructure of cognition, vol. 1, 194-281. MIT Press.
17. Swersky, K.; Chen, B.; Marlin, B.; and de Freitas, N. 2010. A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. In Information Theory and Applications Workshop (ITA), 2010, 1-10. IEEE.
18. Welling, M.; Rosen-Zvi, M.; and Hinton, G. 2005. Exponential family harmoniums with an application to information retrieval. In Saul, L. K.; Weiss, Y.; and Bottou, L., eds., Advances in Neural Information Processing Systems 17. Cambridge, MA: MIT Press. 1481-1488.
19. Yuan, M., and Lin, Y. 2006. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68(1):49-67.