Variational Inference

Fast Second Order Stochastic Backpropagation for Variational Inference NeurIPS 2015 Kullback-Leibler Proximal Variational Inference NeurIPS 2015 Learning Deep Sigmoid Belief Networks with Data Augmentation AISTATS 2015 Learning Structured Output Representation using Deep Conditional Generative Models NeurIPS 2015 Local Expectation Gradients for Black Box Variational Inference NeurIPS 2015 Markov Chain Monte Carlo and Variational Inference: Bridging the Gap ICML 2015 Max-Margin Deep Generative Models NeurIPS 2015 On some provably correct cases of variational inference for topic models NeurIPS 2015 Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks ICML 2015 Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process AISTATS 2015 Scalable Variational Gaussian Process Classification AISTATS 2015 Scalable Variational Inference in Log-supermodular Models ICML 2015 Sensor Selection for Crowdsensing Dynamical Systems AISTATS 2015 Sparse Submodular Probabilistic PCA AISTATS 2015 Sparse Variational Inference for Generalized GP Models ICML 2015 Stochastic Structured Variational Inference AISTATS 2015 Streaming Variational Inference for Bayesian Nonparametric Mixture Models AISTATS 2015 Variational Generative Stochastic Networks with Collaborative Shaping ICML 2015 Variational Inference for Gaussian Process Modulated Poisson Processes ICML 2015 Variational Inference with Normalizing Flows ICML 2015 Weight Uncertainty in Neural Network ICML 2015 A Complete Variational Tracker NeurIPS 2014 Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP NeurIPS 2014 Automated Variational Inference for Gaussian Process Models NeurIPS 2014 Black Box Variational Inference AISTATS 2014