Deep predictive coding networks

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

Volumn , Issue , 2013, Pages

  Chalasani, Rakesh ^a   Principe, Jose C ^a  

^a J Crayton Pruitt Family Department of Biomedical Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP LEARNING; EXTRACTION; FEATURE EXTRACTION;

CONTEXT SENSITIVE; GENERATIVE MODEL; INVARIANT REPRESENTATION; LEARNING METHODS; PREDICTIVE CODING; QUALITY OF DATA; STRUCTURED NOISE; TIME VARYING SIGNAL;

NETWORK CODING;

EID: 85083949938     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. David G. Lowe. Object recognition from local scale-invariant features. In Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2, ICCV’99, pages 1150–, 1999. ISBN 0-7695-0164-8.
2. Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Volume 1 - Volume 01, CVPR’05, pages 886–893, 2005. ISBN 0-7695-2372-2.
3. B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583):607–609, June 1996. ISSN 0028-0836.
4. L. Wiskott and T.J. Sejnowski. Slow feature analysis: Unsupervised learning of invariances. Neural computation, 14(4):715–770, 2002.
5. Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML’09, pages 609–616, 2009. ISBN 978-1-60558-516-1.
6. K. Kavukcuoglu, P. Sermanet, Y.L. Boureau, K. Gregor, M. Mathieu, and Y. LeCun. Learning convolutional feature hierarchies for visual recognition. Advances in Neural Information Processing Systems, pages 1090–1098, 2010.
7. M.D. Zeiler, D. Krishnan, G.W. Taylor, and R. Fergus. Deconvolutional networks. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2528–2535. IEEE, 2010.
8. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.A. Manzagol. Stacked denoising autoen-coders: Learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research, 11:3371–3408, 2010.
9. Rajesh P. N. Rao and Dana H. Ballard. Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Computation, 9:721–763, 1997.
10. Karl Friston. Hierarchical models in the brain. PLoS Comput Biol, 4(11):e1000211, 11 2008.
11. Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. A Fast Learning Algorithm for Deep Belief Nets. Neural Comp., (7):1527–1554, July .
12. Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. Greedy layer-wise training of deep networks. In In NIPS, 2007.
13. Koray Kavukcuoglu, Marc’Aurelio Ranzato, and Yann LeCun. Fast inference in sparse coding algorithms with applications to object recognition. CoRR, abs/1010.3467, 2010.
14. Yan Karklin and Michael S. Lewicki. A hierarchical bayesian model for learning nonlinear statistical regularities in nonstationary natural signals. Neural Computation, 17:397–423, 2005.
15. D. Angelosante, G.B. Giannakis, and E. Grossi. Compressed sensing of time-varying signals. In Digital Signal Processing, 2009 16th International Conference on, pages 1 –8, july 2009.
16. A. Charles, M.S. Asif, J. Romberg, and C. Rozell. Sparsity penalties in dynamical system estimation. In Information Sciences and Systems (CISS), 2011 45th Annual Conference on, pages 1 –6, march 2011.
17. D. Sejdinovic, C. Andrieu, and R. Piechocki. Bayesian sequential compressed sensing in sparse dynamical systems. In Communication, Control, and Computing (Allerton), 2010 48th Annual Allerton Conference on, pages 1730 –1736, 29 2010-oct. 1 2010. doi: 10.1109/ALLERTON. 2010.5707125.
18. X. Chen, Q. Lin, S. Kim, J.G. Carbonell, and E.P. Xing. Smoothing proximal gradient method for general structured sparse regression. The Annals of Applied Statistics, 6(2):719–752, 2012.
19. Amir Beck and Marc Teboulle. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. SIAM Journal on Imaging Sciences, (1):183–202, March . ISSN 19364954. doi: 10.1137/080716542.
20. Y. Nesterov. Smooth minimization of non-smooth functions. Mathematical Programming, 103 (1):127–152, 2005.
21. Karol Gregor and Yann LeCun. Efficient Learning of Sparse Invariant Representations. CoRR, abs/1105.5307, 2011.
22. Alex Nelson. Nonlinear estimation and modeling of noisy time-series by dual Kalman filtering methods. PhD thesis, 2000.
23. 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 Comput., 1 (4):541–551, December 1989. ISSN 0899-7667. doi: 10.1162/neco.1989.1.4.541. URL http://dx.doi.org/10.1162/neco.1989.1.4.541.
24. H. Zou and T. Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301–320, 2005.