Deep Generative Stochastic Networks Trainable by Backprop
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Yoshua Bengio | Jason Yosinski | Guillaume Alain | Eric Thibodeau-Laufer | Yoshua Bengio | J. Yosinski | Eric Thibodeau-Laufer | Guillaume Alain
[1] P. Schweitzer. Perturbation theory and finite Markov chains , 1968 .
[2] Michael I. Jordan,et al. Exploiting Tractable Substructures in Intractable Networks , 1995, NIPS.
[3] H. Sebastian Seung,et al. Learning Continuous Attractors in Recurrent Networks , 1997, NIPS.
[4] David Maxwell Chickering,et al. Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..
[5] Sven Behnke,et al. Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid , 2001, Int. J. Comput. Intell. Appl..
[6] C. D. Meyer,et al. Comparison of perturbation bounds for the stationary distribution of a Markov chain , 2001 .
[7] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[8] Aapo Hyvärinen,et al. Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines , 2006, Neural Computation.
[9] Fu Jie Huang,et al. A Tutorial on Energy-Based Learning , 2006 .
[10] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[11] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[12] Rajat Raina,et al. Efficient sparse coding algorithms , 2006, NIPS.
[13] Fernando Pereira,et al. Structured Learning with Approximate Inference , 2007, NIPS.
[14] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[15] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[16] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[17] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[18] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[19] Geoffrey E. Hinton,et al. Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine , 2010, NIPS.
[20] Geoffrey E. Hinton,et al. Binary coding of speech spectrograms using a deep auto-encoder , 2010, INTERSPEECH.
[21] Aapo Hyvärinen,et al. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.
[22] Dong Yu,et al. Conversational Speech Transcription Using Context-Dependent Deep Neural Networks , 2012, ICML.
[23] Pedro M. Domingos,et al. Sum-product networks: A new deep architecture , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).
[24] Pascal Vincent,et al. Quickly Generating Representative Samples from an RBM-Derived Process , 2011, Neural Computation.
[25] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[26] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[27] F. Savard. Réseaux de neurones à relaxation entraînés par critère d'autoencodeur débruitant , 2012 .
[28] Klaus-Robert Müller,et al. Deep Boltzmann Machines and the Centering Trick , 2012, Neural Networks: Tricks of the Trade.
[29] Yoshua Bengio,et al. A Generative Process for sampling Contractive Auto-Encoders , 2012, ICML 2012.
[30] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[31] Pascal Vincent,et al. Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.
[32] Yoshua Bengio,et al. Estimating or Propagating Gradients Through Stochastic Neurons , 2013, ArXiv.
[33] Yoshua Bengio,et al. Multi-Prediction Deep Boltzmann Machines , 2013, NIPS.
[34] Diederik P. Kingma. Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form , 2013, ArXiv.
[35] Yoshua Bengio,et al. Better Mixing via Deep Representations , 2012, ICML.
[36] Pascal Vincent,et al. Generalized Denoising Auto-Encoders as Generative Models , 2013, NIPS.
[37] Jason Weston,et al. A semantic matching energy function for learning with multi-relational data , 2013, Machine Learning.
[38] Yoshua Bengio,et al. Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions , 2012, AISTATS.
[39] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[40] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.