Variational Inference

A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models ICML 2016 A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models AISTATS 2016 An Architecture for Deep, Hierarchical Generative Models NeurIPS 2016 Autoencoding beyond pixels using a learned similarity metric ICML 2016 Auxiliary Deep Generative Models ICML 2016 BayesPy: Variational Bayesian Inference in Python JMLR 2016 Bidirectional Helmholtz Machines ICML 2016 Clamping Improves TRW and Mean Field Approximations AISTATS 2016 Deep Gaussian Conditional Random Field Network: A Model-Based Deep Network for Discriminative Denoising CVPR 2016 Disease Trajectory Maps NeurIPS 2016 d-VMP: Distributed Variational Message Passing PGM 2016 Early Stopping as Nonparametric Variational Inference AISTATS 2016 f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization NeurIPS 2016 Generating Images with Perceptual Similarity Metrics based on Deep Networks NeurIPS 2016 Hierarchical Variational Models ICML 2016 Improved Variational Inference with Inverse Autoregressive Flow NeurIPS 2016 Iterative Refinement of the Approximate Posterior for Directed Belief Networks NeurIPS 2016 Ladder Variational Autoencoders NeurIPS 2016 Learning to Generate with Memory ICML 2016 Linear dynamical neural population models through nonlinear embeddings NeurIPS 2016 Modeling and Transforming Speech Using Variational Autoencoders Interspeech 2016 Neural Variational Inference for Text Processing ICML 2016 On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes AISTATS 2016 Operator Variational Inference NeurIPS 2016 Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines AISTATS 2016