이진 신경망의 학습과 활용

  • Kim, Min-Je (University of Illinois at Urbana-Champaign)
  • Published : 2015.08.27

Abstract

Keywords

References

  1. G. E. Hinton, S. Osindero, and Y. The, "A fast learning algorithm for deep belief nets," Neural Computation, 18 (7): 1527-1554, 2006. https://doi.org/10.1162/neco.2006.18.7.1527
  2. Y. Bengio, "Learning deep architectures for AI," Foundations and Trends in Machine Learning, 2(1): 1-127, 2009. https://doi.org/10.1561/2200000006
  3. G. Cybenko, "Approximations by superpositions of sigmoidal functions," Mathematics of Control, Signals, and Systems, 2(4):303-314, 1989. https://doi.org/10.1007/BF02551274
  4. K. Hornik, "Approximation capabilities of multilayer feedforward networks," Neural Networks, 4(2):251-257, 1991. https://doi.org/10.1016/0893-6080(91)90009-T
  5. M. Baldauf, S. Dustdar, and F. Rosenberg, "A survey on context-aware systems," International Journal of Ad Hoc and Ubiquitous Computing, 2(4):263-277 , January 2007. https://doi.org/10.1504/IJAHUC.2007.014070
  6. M. Courbariaux, Y. Bengio, and J.-P. David, "Low precision arithmetic for deep learning," arXiv preprint arXiv: 1412.7024, 2014.
  7. E. Fiesler, A, Chou dry, and H. J. Caulfield, "Weight discretization paradigm for optical neural networks," In The Hague '90, 12-16 April, pp. 164-173. International Society for Optics and Photonics, 1990.
  8. K. Hwang, and W. Sung, "Fixed-point feedforward deep neural network design using weights + 1, 0, and-1, " In 2014 IEEE Workshop on Signal Processing Systems (SiPS), Oct 2014.
  9. M. Kim and P. Smaragdis, "Bitwise Neural Networks," International Conference on Machine Learning Workshop on Resource-Efficient Machine Learning, 2015.
  10. D. Soudry, J. Hubara, and R. Meir, "Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights," In Advances in Neural Information Processing Systems (NIPS), 2014.
  11. W. S. McCulloch, and W. H. Pitts, "A logical calculus of the ideas immanent in nervous activity," The Bulletin of Mathematical Biophysics, 5(4):115-133, 1943. https://doi.org/10.1007/BF02478259
  12. T. Dean, M. A. Ruzon, M. Segal, J. Shlens, S. Vijayanarasirnhan, and J. Yagnik, "Fast, accurate detection of 100,000 object classes on a single machine," in Proceedings ofthe IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
  13. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, 86(11):2278-2324, November 1998. https://doi.org/10.1109/5.726791
  14. N. Srivastava, G. E. Hinton, A. Krizhevsky, J. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," Journal of Machine Learning Research, 15(1):1929-1958, January 2014.