Saturating auto-encoders

1st International Conference on Learning Representations, ICLR 2013 - Conference Track Proceedings

Volumn , Issue , 2013, Pages

  Goroshin, Rostislav ^a   LeCun, Yann ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ACTIVATION; LEARNING SYSTEMS;

ACTIVATION FUNCTIONS; AUTO ENCODERS; DATA MANIFOLDS; HIDDEN UNIT ACTIVATION; REGULARIZER; ZERO GRADIENTS;

SIGNAL ENCODING;

EID: 85083950234     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. MarcAurelio Ranzato, Christopher Poultney, Sumit Chopra and Yann LeCun. Efficient Learning of Sparse Representations with an Energy- Based Model, in J. Platt et al. (Eds), Advances in Neural Information Processing Systems (NIPS 2006), 19, MIT Press, 2006.
2. MarcAurelio Ranzato, Fu-Jie Huang, Y-Lan Boureau and Yann LeCun: Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition, Proc. Computer Vision and Pattern Recognition Conference (CVPR’07), IEEE Press, 2007
3. Rifai, S. and Vincent, P. and Muller, X. and Glorot, X. and Bengio, Y. Contractive auto-encoders: Explicit invariance during feature extraction, Proceedings of the Twenty-eight International Conference on Machine Learning, ICML 2011
4. P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol. Extracting and Composing Robust Features with Denoising Autoencoders Proceedings of the 25th International Conference on Machine Learning (ICML’2008), 2008.
5. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, New York: John Wiley & Sons, 2001, pp. xx + 654, ISBN: 0-471-05669-3
6. Olhausen, Bruno A.; Field, David J. (1997). Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1?. Vision Research 37 (23): 3311-3325.
7. Karol Gregor and Yann LeCun: Learning Fast Approximations of Sparse Coding, Proc. International Conference on Machine learning (ICML’10), 2010
8. Guillaume Alain and Yoshua Bengio, What Regularized Auto-Encoders Learn from the Data Generating Distribution. arXiv:1211.4246v3 [cs.LG]