variational inference

Ladder Variational Autoencoders NeurIPS 2016 Latent Point Process Allocation AISTATS 2016 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016 Learning to Generate with Memory ICML 2016 Linear dynamical neural population models through nonlinear embeddings NeurIPS 2016 Measuring the reliability of MCMC inference with bidirectional Monte Carlo NeurIPS 2016 Mixture of Bilateral-Projection Two-Dimensional Probabilistic Principal Component Analysis CVPR 2016 Natural-Parameter Networks: A Class of Probabilistic Neural Networks NeurIPS 2016 Neural Variational Inference for Text Processing ICML 2016 On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes AISTATS 2016 On the properties of variational approximations of Gibbs posteriors JMLR 2016 Operator Variational Inference NeurIPS 2016 Predicting Disease Progression with a Model for Multivariate Longitudinal Clinical Data MLHC 2016 Provable Bayesian Inference via Particle Mirror Descent AISTATS 2016 Relevant sparse codes with variational information bottleneck NeurIPS 2016 Rényi Divergence Variational Inference NeurIPS 2016 Scalable Gaussian Process Classification via Expectation Propagation AISTATS 2016 Select-and-Sample for Spike-and-Slab Sparse Coding NeurIPS 2016 Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues JMLR 2016 Sequential Inference for Deep Gaussian Process AISTATS 2016 Sequential Neural Models with Stochastic Layers NeurIPS 2016 Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm NeurIPS 2016 Stochastic Variational Deep Kernel Learning NeurIPS 2016 Stochastic Variational Inference for the HDP-HMM AISTATS 2016 Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors ICML 2016