Generalized Denoising Auto-Encoders as Generative Models
暂无分享,去创建一个
Pascal Vincent | Li Yao | Yoshua Bengio | Guillaume Alain | Yoshua Bengio | Guillaume Alain | Pascal Vincent | L. Yao
[1] Geoffrey E. Hinton. Products of experts , 1999 .
[2] David Maxwell Chickering,et al. Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..
[3] Aapo Hyvärinen,et al. Estimation of Non-Normalized Statistical Models by Score Matching , 2005, J. Mach. Learn. Res..
[4] Pascal Vincent,et al. Non-Local Manifold Parzen Windows , 2005, NIPS.
[5] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[6] Yoshua Bengio,et al. Nonlocal Estimation of Manifold Structure , 2006, Neural Computation.
[7] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[8] A. Hyvärinen,et al. Estimation of Non-normalized Statistical Models , 2009 .
[9] Yann LeCun,et al. Regularized estimation of image statistics by Score Matching , 2010, NIPS.
[10] Pascal Vincent,et al. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.
[11] Nando de Freitas,et al. On Autoencoders and Score Matching for Energy Based Models , 2011, ICML.
[12] Pascal Vincent,et al. A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.
[13] Yoshua Bengio,et al. A Generative Process for sampling Contractive Auto-Encoders , 2012, ICML 2012.
[14] Pascal Vincent,et al. Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.
[15] Tapani Raiko,et al. Enhanced Gradient for Training Restricted Boltzmann Machines , 2013, Neural Computation.
[16] Yoshua Bengio,et al. Better Mixing via Deep Representations , 2012, ICML.
[17] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[18] Li Yao,et al. Bounding the Test Log-Likelihood of Generative Models , 2014, ICLR.
[19] Yoshua Bengio,et al. Deep Generative Stochastic Networks Trainable by Backprop , 2013, ICML.