A generative process for sampling contractive auto-encoders

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 2, Issue , 2012, Pages 1855-1862

  Rifai, Salah ^a   Bengio, Yoshua ^a   Dauphin, Yann N ^a   Vincent, Pascal ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ERRORS; DATA POINTS; DEEP BELIEF NETWORKS; HIGH-DENSITY REGIONS; INPUT DATAS; INPUT SPACE; JACOBIANS; LOCAL MANIFOLD STRUCTURE; LOCAL STRUCTURE; LOCAL VARIATIONS; RESTRICTED BOLTZMANN MACHINE; SECOND LAYER; SINGULAR VALUES; SINGULAR VECTORS;

METADATA; RANDOM PROCESSES; VECTOR SPACES;

LEARNING SYSTEMS;

EID: 84867136416     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Y. Bengio. Learning deep architectures for AI. Foundations & Trends in Mach. Learn., 2(1):1-127, 2009.
2. Y. Bengio and M. Monperrus. Non-local manifold tangent learning. In NIPS'2004, pages 129-136. MIT Press, 2005.
3. Y. Bengio, H. Larochelle, and P. Vincent. Non-local manifold Parzen windows. In NIPS'2005, pages 115-122. MIT Press, 2006a.
4. Y. Bengio, M. Monperrus, and H. Larochelle. Nonlocal estimation of manifold structure. Neural Computation, 18(10):2509-2528, 2006b.
5. O. Breuleux, Y. Bengio, and P. Vincent. Quickly generating representative samples from an rbm-derived process. Neural Computation, 23(8):2053-2073, Aug. 2011.
6. L. Cayton. Algorithms for manifold learning. Technical Report CS2008-0923, UCSD, 2005.
7. A. Coates and A. Y. Ng. Selecting receptive fields in deep networks. In NIPS'2011, 2011.
8. I. Goodfellow, Q. Le, A. Saxe, and A. Ng. Measuring invariances in deep networks. In NIPS'2009, pages 646-654, 2009.
9. G. E. Hinton, S. Osindero, and Y. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554, 2006.
10. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541-551, 1989.
11. H. Narayanan and S. Mitter. Sample complexity of testing the manifold hypothesis. In NIPS'2010. 2010.
12. M. Ranzato, J. Susskind, V. Mnih, and G. E. Hinton. On deep generative models with applications to recognition. In CVPR'11, pages 2857-2864, 2011.
13. S. Rifai, Y. Dauphin, P. Vincent, Y. Bengio, and X. Muller. The manifold tangent classifier. In NIPS'2011, 2011a. Student paper award.
14. S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio. Contracting auto-encoders: Explicit invariance during feature extraction. In ICML'2011, 2011b.
15. S. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, Dec. 2000.
16. B. Schölkopf, A. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998.
17. P. Simard, B. Victorri, Y. LeCun, and J. Denker. Tangent prop - A formalism for specifying selected invariances in an adaptive network. In NIPS'1991, 1992.
18. J. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, Dec. 2000.
19. M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analysers. Neural Computation, 11(2):443-482, 1999.