Discriminative learning of sum-product networks

Advances in Neural Information Processing Systems

Volumn 4, Issue , 2012, Pages 3239-3247

  Gens, Robert ^a   Domingos, Pedro ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION TASKS; DEEP ARCHITECTURES; DIFFUSION PROBLEMS; DISCRIMINATIVE LEARNING; DISCRIMINATIVE TRAINING; GENERATIVE METHODS; GRADIENT DESCENT; IMAGE STRUCTURES;

BACKPROPAGATION ALGORITHMS; IMAGE CLASSIFICATION; NETWORK ARCHITECTURE; NETWORK LAYERS;

STATISTICAL TESTS;

EID: 84877731706     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. M. Amer and S. Todorovic. Sum-product networks for modeling activities with stochastic structure. CVPR, 2012.
2. F. Bach and M.I. Jordan. Thin junction trees. Advances in Neural Information Processing Systems, 14:569-576, 2002.
3. Y. Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1):1-127, 2009.
4. L. Bo, K. Lai, X. Ren, and D. Fox. Object recognition with hierarchical kernel descriptors. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1729-1736. IEEE, 2011.
5. L. Bo, X. Ren, and D. Fox. Kernel descriptors for visual recognition. Advances in Neural Information Processing Systems, 2010.
6. L. Bo, X. Ren, and D. Fox. Unsupervised feature learning for RGB-D based object recognition. ISER, 2012.
7. C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller. Context-specific independence in bayesian networks. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, pages 115- 123, 1996.
8. M. Chavira and A. Darwiche. On probabilistic inference by weighted model counting. Artificial Intelligence, 172(6-7):772-799, 2008.
9. A. Chechetka and C. Guestrin. Efficient principled learning of thin junction trees. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, MA, 2008.
10. A. Coates, H. Lee, and A.Y. Ng. An analysis of single-layer networks in unsupervised feature learning. In aistats11. Society for Artificial Intelligence and Statistics, 2011.
11. A. Coates and A.Y. Ng. The importance of encoding versus training with sparse coding and vector quantization. In International Conference on Machine Learning, volume 8, page 10, 2011.
12. A. Coates and A.Y. Ng. Selecting receptive fields in deep networks. NIPS, 2011.
13. M. Collins. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, pages 1-8, Philadelphia, PA, 2002. ACL.
14. A. Darwiche. A differential approach to inference in Bayesian networks. Journal of the ACM, 50:280-305, 2003.
15. A. Darwiche. Modeling and Reasoning with Bayesian Networks. Cambridge University Press, 2009.
16. O. Delalleau and Y. Bengio. Shallow vs. deep sum-product networks. In Proceedings of the 25th Conference on Neural Information Processing Systems, 2011.
17. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39:1-38, 1977.
18. P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. Ieee, 2008.
19. A. Hyvärinen and E. Oja. Independent component analysis: algorithms and applications. Neural networks, 13(4-5):411-430, 2000.
20. Y. Jia, C. Huang, and T. Darrell. Beyond spatial pyramids: Receptive field learning for pooled image features. In CVPR, 2012.
21. A. Kulesza, F. Pereira, et al. Structured learning with approximate inference. Advances in Neural Information Processing Systems, 20:785-792, 2007.
22. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling data. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 282-289, Williamstown, MA, 2001. Morgan Kaufmann.
23. H. Poon and P. Domingos. Sum-product networks: A new deep architecture. In Proc. 12th Conf. on Uncertainty in Artificial Intelligence, pages 337-346, 2011.
24. M.A. Ranzato and G.E. Hinton. Modeling pixel means and covariances using factorized third-order Boltzmann machines. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2551-2558. IEEE, 2010.
25. J. Salojärvi, K. Puolamäki, and S. Kaski. Expectation maximization algorithms for conditional likelihoods. In Proceedings of the 22nd international conference on Machine learning, pages 752-759. ACM, 2005.