Learning to represent spatial transformations with factored higher-Order boltzmann machines

Neural Computation

Volumn 22, Issue 6, 2010, Pages 1473-1492

  Memisevic, Roland ^a   Hinton, Geoffrey E ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; DEPTH PERCEPTION; IMAGE PROCESSING; MATHEMATICAL PHENOMENA; METHODOLOGY; PATTERN RECOGNITION; PHYSIOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE PROCESSING, COMPUTER-ASSISTED; MATHEMATICAL CONCEPTS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; SPACE PERCEPTION;

EID: 77953520240     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2010.01-09-953     Document Type: Article

References (20)

1. Bell, A. J., & Sejnowski, T. J. (1997). The independent components of natural scenes are edge filters. Vision Research, 37, 3327-3338.
2. Foldiak, P. (1991). Learning invariance from transformation sequences. Neural Computation, 3(2), 194-200.
3. Grimes, D. B., & Rao, R. P. N. (2005). Bilinear sparse coding for invariant vision. Neural Computation, 17(1), 47-73.
4. Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8), 1771-1800.
5. Hofstadter, D. R. (1984). The copycat project: An experiment in nondeterminism and creative analogies (A.I. Memo 755). Cambridge, MA: Massachusetts Institute of Technology, 755.
6. Memisevic, R. (2008). Non-linear latent factor models for revealing structure in highdimensional data. Unpublished doctoral dissertation, University of Toronto.
7. Memisevic, R., & Hinton, G. (2007). Unsupervised learning of image transformations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE Press.
8. Miao, X., & Rao, R. P. N. (2007). Learning the Lie groups of visual invariance. Neural Computation, 19(10), 2665-2693.
9. Olshausen, B. (1994). Neural routing circuits for forming invariant representations of visual objects. Unpublished doctoral dissertation, California Institute of Technology.
10. Olshausen, B. A., Cadieu, C., Culpepper, J., & Warland, D. K. (2007). Bilinear models of natural images. In SPIE Proceedings: Human Vision Electronic Imaging XII. Bellingham,WA: SPIE.
11. Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583), 607- 609.
12. Rao, R. P., & Ballard, D. H. (1997). Efficient encoding of natural time varying images produces oriented space-time receptive fields (Tech. Rep.). Rochester, NY: University of Rochester.
13. Rao, R. P., & Ruderman, D. L. (1999). Learning Lie groups for invariant visual perception. In M. S. Kearns, S. Solla, & D. Cohn (Eds.), Advances in neural information processing systems, 11. Cambridge, MA: MIT Press.
14. Sejnowski, T. J. (1987). Higher-order Boltzmann machines. In AIP Conference Proceedings 151 on Neural Networks for Computing (pp. 398-403).Woodbury,NY: American Institute of Physics.
15. Simard, P., LeCun, Y.,Denker, J.,&Victorri, B. (1992).An efficient algorithm for learning invariances in adaptive classifiers. In Proceedings of the 11th IAPR International Conference on Pattern Recognition. Conference B: Pattern RecognitionMethodology and Systems. N.p.: IAPR.
16. Taylor, G. W., Hinton, G. E., & Roweis, S. T. (2007). Modeling human motion using binary latent variables. In B. Schölköpf, J. Platt, & T. Hoffman (Eds.), Advances in neural information processing systems, 19. Cambridge, MA: MIT Press.
17. Tenenbaum, J. B., & Freeman,W. T. (2000). Separating style and content with bilinear models. Neural Computation, 12(6), 1247-1283.
18. van Hateren, L., & Ruderman, J. (1998). Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proceedings: Biological Sciences, 265(1412), 2315-2320.
19. Vasilescu, M. A. O., & Terzopoulos, D. (2002). Multilinear analysis of image ensembles: Tensorfaces. In ECCV '02: Proceedings of the 7th European Conference on Computer Vision-Part I (pp. 447-460). Berlin: Springer-Verlag.
20. Welling, M., Rosen-Zvi, M., & Hinton, G. (2005). Exponential family harmoniums with an application to information retrieval. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems, 17. Cambridge, MA: MIT Press.