Bibliografia

General

  • Goodfellow I., Bengio Y., Courville A. "Deep learning". MIT Press, 2016.

 

Memoria asociativa

  • Hopfield J.J., "Neural networks and physical systems with emergent computational abilities", Proc. Nat. Acad. Sc., USA, 79 (Biophysics), 2554-8 (1982).
  • Hopfield, J.J. y Tank, D.W., "Neural computation of decisions in optimization problems", Biol. Cybern 52:141-152 (1985).
  • McKay D.J.C., "Hopfield Networks", en Information Theory, Inference and Learning Algorithms, Cambridge, Cambridge University Press (2003).

 

Maquinas de Boltzmann

  • Kappen H.J., "Deterministic learning rules for Boltzmann Machines", Neural Networks, vol. 8, 537-548 (1995).
  • Hinton G.E., "Learning multiple layers of representation", TRENDS in Cognitive Sciences, vol.11, 10:428-434 (2007).

 

Autoencoders

  • Rumelhart D.E., Hinton G.E. y Williams R.J., "Learning representations by back-propagating errors", Nature, vol. 323, 533-536 (1986).
  • Olshausen B.A. y Field D.J., "Emergence of simple-cell receptive field propeties by learning a sparse code for natural images", Nature, vol. 381, 607-609 (1996).
  • Liou C-Y., Cheng W-C., Liou J-W. y Liou D-R., "Autoencoder for words", Neurocomputing vol.139, 84-96 (2014).
  • Vincent P., Larochelle, H., Lajoie, I., Bengio, Y. y Manzagol, P-A., "Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion", The Journal of Machine Learning Research, 11:3371-3408 (2010).

 

Arquitecturas profundas

  • Salakhutdinov R. y Hinton G.E., "Deep Boltzmann Machines", AISTATS, 448-455 (2009).
  • Bengio Y. y Courville, A., "Deep Learning of Representations", Handbook on Neural Information processing; Bianchini, M., Jain, L., Maggini, M., Eds.; Springer:Berlin Heidelberg (2011).
  • Bengio Y., "Learning Deep Architectures for AI", Foundations and Trends in Machine Learning, vol. 2, 1:1-127 (2009).

 

Redes Convolucionales

  • LeCun Y., Boser B., Denker J.S., Henderson D., Howard R.E., Hubbard W. y Lawrence L.D., "Backpropagation applied to handwritten zip code recognition", Neural Computation 1, 541-551 (1989)
  • LeCun, Y. y Bengio, Y., "Convolutional networks for images, speech, and time series", The handbook of brain theory and neural networks, 3361 (1995).
  • Eigen D., Rolfe J., Fergus R. y LeCun Y., "Understanding Deep Architectures using a Recursive Convolutional Network", International Conference on Learning Representations (ICLR2014), CBLS (2014).

 

Redes Recurrentes

  • Elman J.L., "Finding Structure In Time", Cognitive Science, 14:179-211 (1990).
  • Hochreiter, S. y Schmidhuber, J., "Long short-term memory", Neural computation, 9(8): 1735-1780 (1997).
  • Sutskever, I.., "Training recurrent neural networks" (Doctoral dissertation, University of Toronto) (2013).

 

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