Structured Recurrent Temporal Restricted Boltzmann Machines
暂无分享,去创建一个
Silvio Savarese | Benjamin Kuipers | Honglak Lee | Roni Mittelman | B. Kuipers | Honglak Lee | S. Savarese | Roni Mittelman
[1] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[2] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[3] L. Rabiner,et al. An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.
[4] Michael I. Jordan. Graphical Models , 1998 .
[5] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[6] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[7] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[8] Christian Kohlschein. An introduction to Hidden Markov Models , 2007 .
[9] Geoffrey E. Hinton,et al. Learning Multilevel Distributed Representations for High-Dimensional Sequences , 2007, AISTATS.
[10] Geoffrey E. Hinton,et al. The Recurrent Temporal Restricted Boltzmann Machine , 2008, NIPS.
[11] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[12] Alexandre d'Aspremont,et al. Model Selection Through Sparse Max Likelihood Estimation Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data , 2022 .
[13] Geoffrey E. Hinton,et al. Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine , 2010, NIPS.
[14] James M. Rehg,et al. Temporal causality for the analysis of visual events , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[15] Geoffrey E. Hinton,et al. Modeling pixel means and covariances using factorized third-order boltzmann machines , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[16] Lieven Vandenberghe,et al. Topology Selection in Graphical Models of Autoregressive Processes , 2010, J. Mach. Learn. Res..
[17] Ilya Sutskever,et al. Learning Recurrent Neural Networks with Hessian-Free Optimization , 2011, ICML.
[18] Yoshua Bengio,et al. Unsupervised Models of Images by Spikeand-Slab RBMs , 2011, ICML.
[19] Yoshua Bengio,et al. A Spike and Slab Restricted Boltzmann Machine , 2011, AISTATS.
[20] Geoffrey E. Hinton,et al. Two Distributed-State Models For Generating High-Dimensional Time Series , 2011, J. Mach. Learn. Res..
[21] Joshua B. Tenenbaum,et al. Infinite Dynamic Bayesian Networks , 2011, ICML.
[22] Yoshua Bengio,et al. Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription , 2012, ICML.
[23] Seungyeop Han,et al. Structured Learning of Gaussian Graphical Models , 2012, NIPS.
[24] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[25] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[26] Razvan Pascanu,et al. Advances in optimizing recurrent networks , 2012, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.