Extracting and composing robust features with denoising autoencoders

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 1096-1103

  Vincent, Pascal ^a   Larochelle, Hugo ^a   Bengio, Yoshua ^a   Manzagol, Pierre Antoine ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION THEORY; MACHINE LEARNING; EDUCATION; FEATURE EXTRACTION; LEARNING SYSTEMS; ROBOT LEARNING; UNSUPERVISED LEARNING;

BENCHMARK SUITES; COMPARATIVE EXPERIMENTS; DEEP ARCHITECTURES; DISCRIMINATIVE MODELS; GENERATIVE MODEL; INPUT PATTERNS; INTERMEDIATE REPRESENTATIONS; MANIFOLD LEARNING;

UNSUPERVISED LEARNING; LEARNING ALGORITHMS;

AUTOENCODERS; BENCHMARK SUITES; COMPARATIVE EXPERIMENTS; DE-NOISING; DISCRIMINATIVE MODELS; GENERATIVE MODELS; INPUT PATTERNS; INTERMEDIATE REPRESENTATIONS; MANIFOLD LEARNING; PATTERN CLASSIFICATIONS;

EID: 56449089103     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390294     Document Type: Conference Paper

References (23)

1. Bengio, Y. (2007). Learning deep architectures for AI (Technical Report 1312). Université de Montréal, dept. IRO.
2. Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. Advances in Neural Information Processing Systems 19 (pp. 153-160). MIT Press.
3. Bengio, Y., & Le Cun, Y. (2007). Scaling learning algorithms towards AI. In L. Bottou, O. Chapelle, D. DeCoste and J. Weston (Eds.), Large scale kernel machines. MIT Press.
4. Bishop, C. M. (1995). Training with noise is equivalent to tikhonov regularization. Neural Computation, 7, 108-116.
5. Doi, E., Balcan, D. C., & Lewicki, M. S. (2006). A theoretical analysis of robust coding over noisy overcomplete channels. In Y. Weiss, B. Schölkopf and J. Platt (Eds.), Advances in neural information processing systems 18, 307-314. Cambridge, MA: MIT Press.
6. Doi, E., & Lewicki, M. S. (2007). A theory of retinal population coding. NIPS (pp. 353-360). MIT Press.
7. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15, 3736-3745.
8. Gallinari, P., LeCun, Y., Thiria, S., & Fogelman-Soulie, F. (1987). Memoires associatives distribuees. Proceedings of COGNITIVA 87. Paris, La Villette.
9. Hammond, D., & Simoncelli, E. (2007). A machine learning framework for adaptive combination of signal denoising methods. 2007 International Conference on Image Processing (pp. VI: 29-32).
10. Hinton, G. (1989). Connectionist learning procedures. Artificial Intelligence, 40, 185-234.
11. Hinton, G., & Salakhutdinov, R. (2006). Reducing the dimensionality of data with neural networks. Science, 313, 504-507.
12. Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554.
13. Hopfield, J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the. National Academy of Sciences, USA, 79.
14. th International Conference on Machine Learning (ICML'2007) (pp. 473-480).
15. LeCun, Y. (1987). Modèles connexionistes de l'apprentissage. Doctoral dissertation, Université de Paris VI.
16. Lee, H., Ekanadham, C., & Ng, A. (2008). Sparse deep belief net model for visual area V2. In J. Platt, D. Koller, Y. Singer and S. Roweis (Eds.), Advances in neural information processing systems 20. Cambridge, MA: MIT Press.
17. McClelland, J., Rumelhart, D., & the PDP Research Group (1986). Parallel distributed processing: Explorations in the micro structure of cognition, vol. 2. Cambridge: MIT Press.
18. Memisevic, R. (2007). Non-linear latent factor models for revealing structure in high-dimensional data. Doctoral dissertation, Departement of Computer Science, University of Toronto, Toronto, Ontario, Canada.
19. Ranzato, M., Boureau, Y.-L., & LeCun, Y. (2008). Sparse feature learning for deep belief networks. In J. Platt, D. Koller, Y. Singer and S. Roweis (Eds.), Advances in neural information processing systems 20. Cambridge, MA: MIT Press.
20. Ranzato, M., Poultney, C., Chopra, S., & LeCun, Y. (2007). Efficient learning of sparse representations with an energy-based model. Advances in Neural Information Processing Systems (NIPS 2006). MIT Press.
21. Roth, S., & Black, M. (2005). Fields of experts: a framework for learning image priors. IEEE Conference on Computer Vision and Pattern Recognition (pp. 860-867).
22. Utgoff, P., & Stracuzzi, D. (2002). Many-layered learning. Neural Computation, 14, 2497-2539.
23. Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008). Extracting and composing robust features with denoising autoencoders (Technical Report 1316). Université de Montréal, dept. IRO.