Iterative Refinement of the Approximate Posterior for Directed Belief Networks
暂无分享,去创建一个
Nebojsa Jojic | Vince D. Calhoun | Ruslan Salakhutdinov | Kyunghyun Cho | R. Devon Hjelm | Junyoung Chung | Kyunghyun Cho | R. Salakhutdinov | Junyoung Chung | V. Calhoun | N. Jojic
[1] Tapani Raiko,et al. Techniques for Learning Binary Stochastic Feedforward Neural Networks , 2014, ICLR.
[2] Richard E. Turner,et al. Neural Adaptive Sequential Monte Carlo , 2015, NIPS.
[3] Daan Wierstra,et al. Deep AutoRegressive Networks , 2013, ICML.
[4] Hugo Larochelle,et al. Efficient Learning of Deep Boltzmann Machines , 2010, AISTATS.
[5] Parul Parashar,et al. Neural Networks in Machine Learning , 2014 .
[6] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[7] N. Gordon,et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .
[8] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[9] Frank D. Wood,et al. Inference Networks for Sequential Monte Carlo in Graphical Models , 2016, ICML.
[10] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[11] Man-Suk Oh,et al. Adaptive importance sampling in monte carlo integration , 1992 .
[12] Sergey Levine,et al. MuProp: Unbiased Backpropagation for Stochastic Neural Networks , 2015, ICLR.
[13] Nando de Freitas,et al. An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.
[14] Radford M. Neal. Connectionist Learning of Belief Networks , 1992, Artif. Intell..
[15] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[16] Ruslan Salakhutdinov,et al. On the quantitative analysis of deep belief networks , 2008, ICML '08.
[17] Andriy Mnih,et al. Variational Inference for Monte Carlo Objectives , 2016, ICML.
[18] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[19] Max Welling,et al. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap , 2014, ICML.
[20] Michael I. Jordan,et al. Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..
[21] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[22] Ruslan Salakhutdinov,et al. Learning Stochastic Feedforward Neural Networks , 2013, NIPS.
[23] Yoshua Bengio,et al. Reweighted Wake-Sleep , 2014, ICLR.
[24] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[25] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[26] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.