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[Bengio09]
Yoshua Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, to appear, 2009. [ http ]
[TurianEtal09]
Joseph Turian, James Bergstra, and Yoshua Bengio. Quadratic features and deep architectures for chunking. In North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT), 2009. [ http ]
[BengioEtal09]
Yoshua Bengio, Jerome Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In ICML, 2009. [ http ]
[BengioDelalleau09]
Yoshua Bengio and Olivier Delalleau. Justifying and generalizing contrastive divergence. Neural Computation, 21(6):1601-1621, 2009. [ http ]
[ErhanEtal09]
Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, and Pascal Vincent. The difficulty of training deep architectures and the effect of unsupervised pre-training. pages 153-160, 2009. [ http ]
[ErhanEtal09b]
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. Visualizing higher-layer features of a deep network. Technical Report 1341, Université de Montréal, 2009. [ http ]
[LarochelleEtal09]
Hugo Larochelle, Dumitru Erhan, and Pascal Vincent. Deep learning using robust interdependent codes. pages 312-319, 2009. [ http ]
[LarochelleEtal09b]
Hugo Larochelle, Yoshua Bengio, Jerome Louradour, and Pascal Lamblin. Exploring strategies for training deep neural networks. Journal of Machine Learning Research, 10:1-40, 2009. [ http ]
[DesjardinsBengio08]
Guillaume Desjardins and Yoshua Bengio. Empirical evaluation of convolutional RBMs for vision. Technical Report 1327, 2008. [ http ]
[LarochelleBengio08]
Hugo Larochelle and Yoshua Bengio. Classification using discriminative restricted Boltzmann machines. In Andrew McCallum and Sam Roweis, editors, Proceedings of the 25th Annual International Conference on Machine Learning (ICML 2008), pages 536-543. Omnipress, 2008. [ http ]
[LeRouxBengio08]
Nicolas Le Roux and Yoshua Bengio. Representational power of restricted Boltzmann machines and deep belief networks. Neural Computation, 20(6):1631-1649, 2008. [ http ]
[VincentEtal08]
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. pages 1096-1103, 2008. [ http ]
[TaylorEtal07]
Graham Taylor, Geoffrey E. Hinton, and Sam Roweis. Modeling human motion using binary latent variables. In Bernhard Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19. MIT Press, Cambridge, MA, 2007.
[BengioEtal07]
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. Greedy layer-wise training of deep networks. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 153-160. MIT Press, Cambridge, MA, 2007.
[LarochelleEtal07]
Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra, and Yoshua Bengio. An empirical evaluation of deep architectures on problems with many factors of variation. In Proceedings of the Twenty-Fourth International Conference on Machine Learning (ICML 2007). Corvallis, Oregon, USA, 2007.
[HintonSalakhutdinov06]
Geoffrey E. Hinton and Ruslan Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 313:504-507, July 2006. [ DOI ]
[HintonEtal06]
Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neural Comput., 18(7):1527-1554, 2006. [ DOI ]
[HintonEtal06b]
Geoffrey E. Hinton, Simon Osindero, Max Welling, and Yee-Whye Teh. Unsupervised discovery of non-linear structure using contrastive backpropagation. Cognitive Science, 30(4), 2006.
[HadsellEtal06]
R. Hadsell, S. Chopra, and Yann LeCun. Dimensionality Reduction by Learning an Invariant Mapping. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Volume 2, pages 1735-1742, 2006.
[Carreira-PerpinanHinton05]
M. A. Carreira-Perpinan and Geoffrey E. Hinton. On contrastive divergence learning. Artificial Intelligence and Statistics, 2005, 2005.
[ChenEtal04]
H. Chen, P. Fleury, and A. F. Murray. Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons. Advances in Neural Information Processing System (NIPS2003), 16, 2004.
[WellingEtal04]
Max Welling, Michal Rosen-Zvi, and Geoffrey E. Hinton. Exponential family harmoniums with an application to information retrieval. In NIPS, 2004.
[ThrunEtal04]
Sebastian Thrun, Lawrence K. Saul, and Bernhard Schölkopf, editors. Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]. MIT Press, 2004.
[MemisevicHinton04]
R. Memisevic and Geoffrey E. Hinton. Multiple relational embedding. Advances in Neural Information Processing Systems, 16:505-512, 2004.
[HintonEtal03]
Geoffrey E. Hinton, Max Welling, and Andriy Mnih. Wormholes improve contrastive divergence. In Thrun et al. [ThrunEtal04].
[BengioEtal03]
Yoshua Bengio, R. Ducharme, P. Vincent, and C. Jauvin. A neural probabilistic language model. Journal of Machine Learning Research, 3(6):1137-1155, 2003.
[Hinton02]
Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14(8):1771-1800, 2002.
[WellingHinton02]
Max Welling and Geoffrey E. Hinton. A new learning algorithm for mean field Boltzmann machines. Proceedings of the International Conference on Artificial Neural Networks, pages 351-357, 2002.
[TehHinton01]
Yee-Whye Teh and Geoffrey E. Hinton. Rate-coded Restricted Boltzmann Machines for Face Recognition. Advances in Neural Information Processing Systems, 13:908-914, 2001.
[HintonTeh01]
Geoffrey E. Hinton and Yee-Whye Teh. Discovering multiple constraints that are frequently approximately satisfied. In UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, pages 227-234, San Francisco, CA, USA, 2001. Morgan Kaufmann Publishers Inc.
[PaccanaroHinton00]
Alberto Paccanaro and Geoffrey E. Hinton. Learning distributed representations by mapping concepts and relations into a linear space. In ICML '00: Proceedings of the Seventeenth International Conference on Machine Learning, pages 711-718, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc.
[PaccanaroHinton00b]
Alberto Paccanaro and Geoffrey E. Hinton. Extracting distributed representations of concepts and relations from positive and negative propositions. In IJCNN '00: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2, page 2259, Washington, DC, USA, 2000. IEEE Computer Society.
[HintonBrown00]
Geoffrey E. Hinton and A. D. Brown. Spiking Boltzmann machines. Advances in Neural Information Processing Systems, 12, 2000.
[Hinton99]
Geoffrey E. Hinton. Products of Experts. In Proceedings of the Ninth International Conference on Artificial Neural Networks, volume 1, pages 1-6, 1999.
[HintonGhahramani97]
Geoffrey E. Hinton and Z. Ghahramani. Generative models for discovering sparse distributed representations. Proc. Roy. Soc., 1997.
[HintonZemel97]
Geoffrey E. Hinton and R. S. Zemel. Minimizing description length in an unsupervised neural network, 1997. [ .html ]
[TouretzkyEtal96]
David S. Touretzky, Michael Mozer, and Michael E. Hasselmo, editors. Advances in Neural Information Processing Systems 8, NIPS, Denver, CO, November 27-30, 1995. MIT Press, 1996.
[HintonEtal95]
Geoffrey E. Hinton, Peter Dayan, Brendan J. Frey, and Radford M. Neal. The wake-sleep algorithm for unsupervised neural networks. Science, 268(5214):1158-1161, 1995.
[FreyEtal95]
Brendan J. Frey, Geoffrey E. Hinton, and Peter Dayan. Does the wake-sleep algorithm produce good density estimators? In Touretzky et al. [TouretzkyEtal96], pages 661-667.
[DayanEtal95]
Peter Dayan et al. The Helmholtz machine. Neural Computation, 7(5):889-904, 1995.
[HintonZemel94]
Geoffrey E. Hinton and R. S. Zemel. Autoencoders, minimum description length, and Helmholtz free energy. Advances in Neural Information Processing Systems, 6:3-10, 1994.
[Zemel93]
R. S. Zemel. A Minimum Description Length Framework for Unsupervised Learning. PhD thesis, University of Toronto, 1993.
[Hinton90]
Geoffrey E. Hinton. Deterministic boltzmann learning performs steepest descent in weight-space. pages 128-133, 1990.
[WilliamsHinton90]
CKI Williams and Geoffrey E. Hinton. Mean field networks that learn to discriminate temporally distorted strings. Connectionist Models: Proceedings of the 1990 Summer School, pages 18-22, 1990.
[GallandHinton89]
C. Galland and Geoffrey E. Hinton. Deterministic Boltzmann learning in networks with asymmetric connectivity. Technical report, Technical Report CRG-TR-89, 1989.
[HintonSejnowski86]
Geoffrey E. Hinton and Terrence J. Sejnowski. Learning and relearning in Boltzmann machines. Mit Press Computational Models Of Cognition And Perception Series, pages 282-317, 1986.
[SejnowskiEtal86]
Terrence J. Sejnowski, P. K. Kienker, and Geoffrey E. Hinton. Learning symmetry groups with hidden units: Beyond the perceptron. Physica D, 2(1-3):260-275, 1986.
[KienkerEtal86]
P. K. Kienker, Terrence J. Sejnowski, Geoffrey E. Hinton, and L. E. Schumacher. Separating figure from ground with a parallel network. Perception, 15(2):197-216, 1986.
[AckleyEtal85]
D. H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski. A Learning Algorithm for Boltzmann Machines. Cognitive Science, 9(1):147-169, 1985.
[FahlmanEtal83]
S. E. Fahlman, Geoffrey E. Hinton, and Terrence J. Sejnowski. Massively parallel architectures for ai: Netl, thistle, and boltzmann machines. In Proc. of AAAI-83, pages 109-113, Washington, DC, 1983.
[HintonSejnowski83]
Geoffrey E. Hinton and Terrence J. Sejnowski. Optimal perceptual inference. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 448-453, 1983.
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-- DumitruErhan - 22 Jun 2007

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