딥하이퍼넷 모델

  • Published : 2015.08.27

Abstract

Keywords

Acknowledgement

Supported by : 정보기술진흥센터, 한국연구재단

References

  1. Samuel, A., "Some studies in machine learning using the game of checkers", IBM Journal, Vol. 3, No. 3, pp.210-229, 1959. https://doi.org/10.1147/rd.33.0210
  2. LeCun, Y., Bengio, Y., and Hinton, G.,"Deep learning", Nature, Vol. 521, No. 7553, pp.436-444, 2015. https://doi.org/10.1038/nature14539
  3. Mnih, V. et al., "Human-level control through deep reinforcement learning", Nature, Vol. 518, No. 7540, pp. 529-533, 2015. https://doi.org/10.1038/nature14236
  4. Ha, J.-W., Kim, K.-M, and Zhang, B.-T., "Automated construction of visual-linguistic knowledge via concept learning from cartoon videos", In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), Austin, TX, 2015.
  5. Mordvintsev, A., Olah, C., and Tyka, M., "Inceptionism: Going deeper into neural networks", Google Research Blog. Retrieved June 20, 2015.
  6. Bengio, Y. "Learning deep architectures for AI", Foundations and Trends in Machine Learning, Vol. 2, No. 1, pp.1-127, 2009. https://doi.org/10.1561/2200000006
  7. LeCun, Y., Bottou, L., Orr, G. B., and Muller, K.-R., "Efficient BackProp", In G. Orr and K. Muller. Neural Networks: Tricks of the Trade. Springer., 1998.
  8. Wolpert, D., "Stacked generalization", Neural Networks, Vol. 5, No. 2, pp.241-259, 1992. https://doi.org/10.1016/S0893-6080(05)80023-1
  9. Cybenko, G., "Approximations by superpositions of sigmoidal functions", Mathematics of Control, Signals, and Systems, Vol. 2, No. 4, pp.303-314, 1989. https://doi.org/10.1007/BF02551274
  10. Hornik, K., "Approximation capabilities of multilayer feedforward networks", Neural Networks, Vol. 4, No. 2, pp.251-257, 1991. https://doi.org/10.1016/0893-6080(91)90009-T
  11. Hinton, G. and Salakhutdinov, R., "Reducing the dimensionality of data with neural networks", Science, Vol. 313, pp.504-507, 2006. https://doi.org/10.1126/science.1127647
  12. Nair, V. and Hinton, G., "Rectified linear units improve restricted Boltzmann machines", International Conference on Machine Learning (ICML-2010), 2010.
  13. Zhang, B.-T., "Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory", IEEE Computational Intelligence Magazine, Vol. 3, No. 3, pp.49-63, 2008. https://doi.org/10.1109/MCI.2008.926615
  14. Zhang, B.-T., "Information-theoretic objective functions for lifelong learning", AAAI 2013 Spring Symposium on Lifelong Machine Learning, Stanford University, March 25-27, 2013.
  15. Zhang, B.-T., "Ontogenesis of agency in machines: A multidisciplinary review", AAAI 2014 Fall Symposium on The Nature of Humans and Machines: A Multidisciplinary Discourse, Arlington, VA, 2014.
  16. Zhang, B.-T., Ha, J.-W., and Kang, M. "Sparse population code models of word learning in concept drift", In Proceedings of the 34th Annual Meeting of the Cognitive Science Society (CogSci 2012), 2012.
  17. Zhang, B.-T., Ohm, P., and Mühlenbein, H., "Evolutionary induction of sparse neural trees", Evolutionary Computation, Vol. 5, No. 2, pp.213-236, 1997. https://doi.org/10.1162/evco.1997.5.2.213