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Variational Inference

1946 directly classified papers

Papers per year

Papers

Variational Bayes on Monte Carlo Steroids NIPS 2016
Variational Autoencoder for Deep Learning of Images, Labels and Captions NIPS 2016
Iterative Refinement of the Approximate Posterior for Directed Belief Networks NIPS 2016
An Architecture for Deep, Hierarchical Generative Models NIPS 2016
Bidirectional Helmholtz Machines ICML 2016
Variational Inference for Monte Carlo Objectives ICML 2016
Neural Variational Inference for Text Processing ICML 2016
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors ICML 2016
Auxiliary Deep Generative Models ICML 2016
Learning to Generate with Memory ICML 2016
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models ICML 2016
Hierarchical Variational Models ICML 2016
Variational Dependent Multi-output Gaussian Process Dynamical Systems JMLR 2016
Autoencoding beyond pixels using a learned similarity metric ICML 2016
Modeling and Transforming Speech Using Variational Autoencoders INTERSPEECH 2016
d-VMP: Distributed Variational Message Passing PGM 2016
BayesPy: Variational Bayesian Inference in Python JMLR 2016
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes JMLR 2016
Tight Variational Bounds via Random Projections and I-Projections AISTATS 2016
Early Stopping as Nonparametric Variational Inference AISTATS 2016
A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models AISTATS 2016
Variational Tempering AISTATS 2016
Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines AISTATS 2016
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes AISTATS 2016
Ladder Variational Autoencoders NIPS 2016