One shot learning of simple visual concepts

Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Volumn , Issue , 2011, Pages 2568-2573

  Lake, Brenden M ^a   Salakhutdinov, Ruslan ^a   Gross, Jason ^a   Tenenbaum, Joshua B ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Bayesian modeling; category learning; neural networks; transfer learning

Indexed keywords

BAYESIAN NETWORKS; LEARNING SYSTEMS;

BAYESIAN MODELLING; CATEGORY LEARNING; COMPUTATIONAL MODELLING; HAND-WRITTEN CHARACTERS; LEARN+; NEURAL-NETWORKS; ONE-SHOT LEARNING; SIMPLE++; TRANSFER LEARNING; VISUAL CONCEPT;

NEURAL NETWORKS;

EID: 85135606166     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Austerweil, J., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric bayesian inference. In Advances in Neural Information Processing Systems.
2. Babcock, M. K., & Freyd, J. (1988). Perception of dynamic information in static handwritten forms. American Journal of Psychology, 101(1), 111-130.
3. Branson, K. (2004). A practical review of uniform b-splines.
4. Carey, S., & Bartlett, E. (1978). Acquiring a single new word. Papers and Reports on Child Language Development, 15, 17-29.
5. Dewar, K., & Xu, F. (in press). Induction, overhypothesis, and the origin of abstract knowledge: evidence from 9-month-old infants. Psychological Science.
6. Edelman, S., Flash, T., & Ullman, S. (1990). Reading cursive handwriting by alignment of letter prototypes. International Journal of Computer Vision, 5(3), 303-331.
7. Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 594-611.
8. Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). Learning phonetic categories by learning a lexicon. In Proceedings of the 31st Annual Conference of the Cognitive Science Society.
9. Freyd, J. (1983). Representing the dynamics of a static form. Memory & Cognition, 11(4), 342-346.
10. Hinton, G. E., & Nair, V. (2006). Inferring motor programs from images of handwritten digits. In Advances in Neural Information Processing Systems (Vol. 18). Cambridge, MA: MIT Press.
11. Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321.
12. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2323.
13. Miller, E. G., Matsakis, N. E., & Viola, P. A. (2000). Learning from one example through shared densities on transformations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
14. Revow, M., Williams, C. K. I., & Hinton, G. E. (1996). Using generative models for handwritten digit recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6), 592-606.
15. Salakhutdinov, R., & Hinton, G. E. (2009). Deep boltzmann machines. In 12th Internationcal Conference on Artificial Intelligence and Statistics.
16. Schyns, P. G., Goldstone, R. L., & Thibaut, J.-P. (1998). The development of features in object concepts. Behavioral and Brain Sciences, 21, 1-54.
17. Smith, L., Jones, S. S., Landau, B., Gershkoff-Stowe, L., & Samuelson, L. (2002). Object name learning provides on-the-job training for attention. Psychological Science, 13, 13-19.
18. Torralba, A., Murphy, K. P., & Freeman, W. T. (2007). Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 854-869.
19. Tse, P. U., & Cavanagh, P. (2000). Chinese and americans see opposite apparent motions in a chinese character. Cognition, 74, B27-B32.