Cognitive Computing III: Deep Dynamic Prediction - 실시간 예측결정 추론

  • Published : 2012.01.18

Abstract

Keywords

References

  1. Bar, M. (Eds.), Predictions in the Brain: Using Our Past to Generate a Future, Oxford University Press, 2011 .
  2. Von der Malsburg, C., Phillips, W. A., and Singer, W. (Eds.), Dynamic Coordination in the Brain: From Neurons to Mind, MIT Press, 2010.
  3. Hebb, D., The Organization of Behavior-A Neuropsychological Theory, Wiley, 1949.
  4. Lashley, K. S., Brain Mechanisms and Intelligence (2nd Ed.), Dover Publications, 1963 .
  5. Sendhoff, B., Korner, E., Spoms, O., Ritter, H., & Doya, K. (Eds.), Creating Brain-Like Intelligence: From Basic Principles to Complex Intelligent Systems, Springer-Verlag, 2009.
  6. Modha, D. S., Ananthanarayanan, R., Esser, S. K., Ndirango, A., Sherbondy, A.J., & Singh, R, Cognitive computing, Communications of the ACM, 54(8):62-71, 2011.
  7. Marr, D., Vision, Freeman and Company, 1982.
  8. 장병탁, 여무송, Cognitive Computing I: Multisensory Perceptual Intelligence-실세계 지각행동 지능, 정보과학회지, 30(1):75-87, 2012.
  9. 장병탁, 이동훈, Cognitive Computing II: Machine Vision-Language Learning-실생활 시각언어 학습, 정보과학회지, 30(1):88-100, 2012.
  10. Rogers, T. & McClelland, J., Semantic cognition: a parallel distributed processing approach, MIT Press, 2006.
  11. Hnton, G. & Anderson, J. A., Parallel Models of Associative Memory, Erlbaum, 1981.
  12. Feldman, J. A. & Ballard, D. H., Connectionist models and their properties, Cognitive Science, 6:205-254, 1982. https://doi.org/10.1207/s15516709cog0603_1
  13. Hopfield, J. J, Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences of the USA, 79(8):2554-2558,1982. https://doi.org/10.1073/pnas.79.8.2554
  14. Rumelhart, D. & McClelland, J., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, 1986.
  15. Bishop, C., Neural Networks for Pattern Recognition, Oxford University Press, 1995.
  16. Mackay, D. J. C., Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003.
  17. Koller, D., Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.
  18. Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A., Bayesian inference with probabilistic population codes, Nature Neuroscience, 9: 1432-1438, 2006. https://doi.org/10.1038/nn1790
  19. Pouget, A., Dayan, P., & Zemel, R. S., Inference and computation with population codes, Annual Review of Neuroscience, 26:381-410, 2003. https://doi.org/10.1146/annurev.neuro.26.041002.131112
  20. Port, R.F. & van Gelder, T., Mind as Motion: Explorations in the Dynamics of Cognition, MIT Press, 1995.
  21. Kelso,J. A. S., Dynamic Patterns: the Self-organization of Brain and Behavior, MIT Press, 1995.
  22. Spivey, M., The Continuity of Mind, Oxford University Press, 2007.
  23. Knill, D. & Richards, W (Eds.), Perception as Bayesian Inference, Cambridge University Press, 1996.
  24. Rao, R., Olshausen, B. A., Lewicki, M. S. (Eds.), Probabilistic Models of the Brain: Perception and Neural Function, MIT Press, 2002.
  25. Knill, D. & Pouget, A., The Bayesian brain: the role of uncertainty in neural coding and computation, Trends in Neurosciences, 27(12):712-719, 2004. https://doi.org/10.1016/j.tins.2004.10.007
  26. Doya, K., Ishii, S., Pouget, A., & Rao, R. (Eds.), Bayesian Brain: Probabilistic Approaches to Neural Coding, MIT Press, 2007.
  27. Ernst, M.O. & Banks, M.S., Humans integrate visual and haptic information in a statistically optimal fashion, Nature, 415:429-433, 2002. https://doi.org/10.1038/415429a
  28. Kording, K. P. & Wolpert, D. M., Bayesian integration in sensorimotor learning, Nature, 427:244-247, 2004. https://doi.org/10.1038/nature02169
  29. Trommershaeuser, J., Koerding, K., and Landy, M. S. (Eds.), Sensory Cue Integration, Oxford University Press, 2011.
  30. Chater, N. & Oaksford, M. (Eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science, Oxford University Press, 2008.
  31. Griffiths, T., Charles Kemp, C. &Tenenbaum, J., Bayesian models of cognition, Sun, R (Ed.) Cambridge Handbook of Computational Psychology, 2008.
  32. Oaksford, M. & Chater, N., Cognition and Conditionals: Probability and Logic in Human Thinking, Oxford Univ. Press, 2010.
  33. DiVincenzo, D. P., Quantum computation, Science, 270 (5234):255-261, 1995. https://doi.org/10.1126/science.270.5234.255
  34. Lee, J.-H., Lee, S. H., Chung, W.-H., Lee, E. S., Park, T. H., Deaton, R., & Zhang, B.-T., A DNA assembly model of sentence generation, BioSystems, 106:51-56, 2011. https://doi.org/10.1016/j.biosystems.2011.06.007
  35. Bennett, C., The thermodynamics of computation-a review, International Journal of Theoretical Physics, 1982.
  36. Adleman, L., Molecular computation of solutions to combinatorial problems, Science, 266(5187): 1021-1024, 1994. https://doi.org/10.1126/science.7973651
  37. Lim, H.-W., Lee, S.H., Yang, K.-A., Lee, J.Y., Yoo, S.-I., Park, T.H. & Zhang, B.-T., In vitro molecular pattern classification via DNA-based weighted sum operation, BioSystems, 100(1):1-7,2010. https://doi.org/10.1016/j.biosystems.2009.12.001
  38. Zhang, B.-T., Self-development learning: constructing optimal size neural networks via incremental data selection, Arbeitspapiere der German National Research Center for Computer Science (GMD), No. 768, 1993.
  39. Hinton, G. & Salakhutdinov, R., Reducing the dimensionality of data with neural networks, Science, 313 (5786):504-507,2006. https://doi.org/10.1126/science.1127647
  40. LeCun, Y. & Bengio, Y., Convolutional Networks for Images Speech and Time Series, The Handbook of Brain Theory and Neural Networks, MIT Press, 1995.
  41. Hawkins, J. & Blakeslee, S., On Intelligence, Times Books, 2005.
  42. Friston, K., Hierarchical models in the brain, PLoS Computational Biololgy, 4(11): e1000211, 2008. https://doi.org/10.1371/journal.pcbi.1000211
  43. Nilsson, N. J., Eye on the prize, AI Magazine, 16(2): 9-17,1995.