Korea Information Processing Society Review (정보처리학회지)

Korea Information Processing Society (한국정보처리학회)
권호 표지

딥러닝 소개 및 주요 이슈

  • Published : 2015.01.28
Download PDF
⟨ Previous Next ⟩

Abstract

Keywords

References

  1. G. Hinton, R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science , Vol. 313, No. 5786, pp. 504-507, Jul. 2006. https://doi.org/10.1126/science.1127647
  2. A. Krizhevsky, I. Sutskever, G. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing (NIPS), Lake Taho, NV, 2012.
  3. J. Markoff, "How Many Computers to Identify a Cat? 16,000," New York Times. June 25, 2012.
  4. J. Markoff, "Scientists See Promise in Deep-Learning Programs," New York Times. November 24, 2012.
  5. G. Marcus, "Is 'Deep Learning' a Revolution in Artificial Intelligence?" The New Yorker, November 25, 2012.
  6. G. Hinton, S. Osindero, Y. Teh, "A fast learning algorithm for deep belief nets," Neural Computation Vol.18, pp. 1527-1554, 2006. https://doi.org/10.1162/neco.2006.18.7.1527
  7. M. Minsky, S. Papert, Perceptrons, Cambridge, MA: MIT Press, 1969.
  8. D. E. Rumelhart, G. E. Hinton, R. J. Williams, "Learning internal representations by error propagation" in Parallel Distributed Processing, MIT Press, 1986, pp. 318-362.
  9. K. Fukushima, "Neocognitron: A self-organizing neural network for a mechanism of pattern recognition unaffected by shift in position," Biological Cybernetics, Vol. 36, No. 4, pp.193-202, 1980. https://doi.org/10.1007/BF00344251
  10. A. Graves, J. Schmidhuber, "Framewise phoneme classification with bidirectional LSTM and other neural network architectures," Neural Networks, Vol. 18, No. 5-6, pp.602-610, 2005. https://doi.org/10.1016/j.neunet.2005.06.042
  11. P. Baldi, P. J. Sadowski, "Understanding dropout," Advances in Neural Information Processing Systems (NIPS), 2013, pp.2814-2822.
  12. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford: Oxford University Press, 1995.
  13. M. Riesenhuber, T. Poggio, "Hierarchical models of object recognition in cortex," Nature Neuroscience, Vol. 2, No. 11, pp.1019-1025, 1999. https://doi.org/10.1038/14819
  14. R. Kurzweil, How to Create a Mind: The Secret of Human Thought Revealed. Penguin Books, 2012.
  15. S. J. Thorpe, M. Fabre-Thorpe, "Seeking Categories in the Brain," Science . Vol. 291, No. 5502, pp.260-262, Jan. 2001. https://doi.org/10.1126/science.1058249
  16. A. Graves, A. Mohamed, G. Hinton. "Speech Recognition with Deep Recurrent Neural Networks," International Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013, Vancouver, Canada.
  17. Y. Bengio, A. Courville, P. Vincent, "Representation learning: A review and new perspectives," Pattern Analysis and Machine Intelligence, IEEE Transactions on , Vol. 35, No. 8, pp.1798-1828, 2013. https://doi.org/10.1109/TPAMI.2013.50
  18. P. Smolensky, "Information Processing in Dynamical Systems: Foundations of Harmony Theory," in Parallel distributed processing: Explorations in the microstructure of cognition , Vol. 1 , MIT Press, Cambridge, MA, 1986, pp. 194-281.
  19. D. C. Ciresan, U. Meier, J. Masci, J. Schmidhuber, "A committee of neural networks for traffic sign classification," In Proc. of International Joint Conference on Neural Networks (IJCNN), 2011, pp.1918-1921.
  20. Y. Bengio, P. Simard, P. Frasconi, "Learning long-term dependencies with gradient descent is difficult," IEEE Transactions on Neural Networks, Vol. 5, No. 2, pp.157-166, 1994. https://doi.org/10.1109/72.279181
  21. Y. Jia, "Caffe: An Open Source Convolutional Architecture for Fast Feature Embedding," 2013, http://caffe.berkeleyvision.org/.
  22. T. Mikolov, W.-T. Yih, G. Zweig, "Linguistic Regularities in Continuous Space Word Representations," In Proc. of NAACL HLT, 2013.
  23. R. Socher, M. Ganjoo, H. Sridhar, O. Bastani, C. D. Manning, A. Y. Ng, "Zero-shot learning through cross-modal transfer," In Proc. of International Conference on Learning Representations (ICLR), Scottsdale, AZ, 2013.
  24. Y. Bengio, E. Thibodeau-Laufer, G. Alain, J. Yosinski, "Deep Generative Stochastic Networks Trainable by Backprop," In Proc. of International Conference on Machine Learning (ICML), 2014.
  25. A. Graves. "Generating Sequences With Recurrent Neural Networks," 2014.
  26. D. Marr, "Vision: A Computational Investigation into Human Representation and Processing of Visual Information," Freeman, San Francisco, 1982.
  27. R. A. Brooks, "Elephants Don't Play Chess," Robotics and Autonomous Systems, Vol. 6, pp.3-15, 1990. https://doi.org/10.1016/S0921-8890(05)80025-9
  28. J. Schmidhuber, "Deep Learning in Neural Networks: An Overview," Technical Report IDSIA-03-14, 2014.
  29. C. Farabet, B. Martini, B. Corda, P. Akselrod, E. Culurciello, Y. LeCun, "NeuFlow: A Runtime Reconfigurable Dataflow Processor for Vision", in Proc. of the Fifth IEEE Workshop on Embedded Computer Vision (ECV), Colorado Springs, 2011.
  30. http://spectrum.ieee.org/robotics/artificial-intelligence/machinelearning-maestro-michael-jordan-on-the-delusions-of-big-data-and-other-huge-engineering-efforts
  31. L. Deng, "Three classes of deep learning architectures and their applications: a tutorial survey," APSIPA Transactions on Signal and Information Processing, 2012.