On random weights and unsupervised feature learning

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Volumn , Issue , 2011, Pages 1089-1096

  Saxe, Andrew M ^a   Koh, Pang Wei ^a   Chen, Zhenghao ^a   Bhand, Maneesh ^a   Suresh, Bipin ^a   Ng, Andrew Y ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE LEARNING; FREQUENCY-SELECTIVE; LEARNING PROCESS; RANDOM WEIGHT; STATE-OF-THE-ART METHODS; TRANSLATION INVARIANTS;

FEATURE EXTRACTION; LEARNING SYSTEMS; OBJECT RECOGNITION;

ARCHITECTURE;

EID: 80053448548     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Bengio, Y. and LeCun, Y. Scaling learning algorithms towards A.I. In Bottou, L., Chapelle, O., and Weston, J. (eds.), Large-Scale Kernel Machines, pp. 321-360. MIT Press, 2007.
2. Coates, A., Lee, H., and Ng, A. Y. An analysis of single-layer networks in unsupervised feature learning. In International Conference on Artificial Intelligence and Statistics, 2011.
3. Gray, R. M. Toeplitz and Circulant Matrices: A review. Foundations and Trends in Communications and Information Theory, 2(3):155-239, 2006.
4. Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. What is the best multi-stage architecture for object recognition? In IEEE 12th International Conference on Computer Vision, pp. 2146-2153, 2009.
5. Krizhevsky, A. Learning multiple layers of features from tiny images. Master's thesis, University of Toronto, 2009.
6. Le, Q.V., Ngiam, J., Chen, Z., Koh, P., Chia, D., and Ng, A. Tiled convolutional neural networks. In Advances in Neural Information Processing Systems 23, 2010.
7. LeCun, Y., Huang, F.J., and Bottou, L. Learning methods for generic object recognition with invariance to pose and lighting. In Conference on Computer Vision and Pattern Recognition, 2004.
8. Nair, V. and Hinton, G. 3D Object Recognition with Deep Belief Nets. In Advances in Neural Information Processing Systems 22, 2009.
9. Pinto, N. and Cox, D.D. Beyond simple features: A large-scale feature search approach to unconstrained face recognition. In IEEE International Conference on Automatic Face and Gesture Recognition, 2011.
10. Pinto, N., Doukhan, D., DiCarlo, J. J., and Cox, D. D. A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Computational Biology, 5(11), 2009.
11. Salakhutdinov, R. and Larochelle, H. Efficient learning of deep boltzmann machines. In International Conference on Artificial Intelligence and Statistics, 2010.
12. Schmidt, M. minfunc. 2005. URL http://www.cs.ubc.ca/~schmidtm/Software/ minFunc.html.
13. Tee, G. J. Eigenvectors of block circulant and alternating circulant matrices. New Zealand Journal of Mathematics, 36:195-211, 2007.