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Variational Inference

1946 directly classified papers

Papers per year

Papers

VAE Learning via Stein Variational Gradient Descent NIPS 2017
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders ACL 2017
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures ICML 2017
Prediction and Control with Temporal Segment Models ICML 2017
Discovering Discrete Latent Topics with Neural Variational Inference ICML 2017
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks ICML 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks ICML 2017
Dropout Inference in Bayesian Neural Networks with Alpha-divergences ICML 2017
Grammar Variational Autoencoder ICML 2017
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms AISTATS 2017
Random Feature Expansions for Deep Gaussian Processes ICML 2017
GPflow: A Gaussian Process Library using TensorFlow JMLR 2017
Improved Variational Inference with Inverse Autoregressive Flow NIPS 2016
Operator Variational Inference NIPS 2016
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization NIPS 2016
Stochastic Variational Deep Kernel Learning NIPS 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm NIPS 2016
Variational Inference in Mixed Probabilistic Submodular Models NIPS 2016
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages NIPS 2016
Rényi Divergence Variational Inference NIPS 2016
Linear dynamical neural population models through nonlinear embeddings NIPS 2016
Disease Trajectory Maps NIPS 2016
Sequential Neural Models with Stochastic Layers NIPS 2016
Deep Gaussian Conditional Random Field Network: A Model-Based Deep Network for Discriminative Denoising CVPR 2016
Clamping Improves TRW and Mean Field Approximations AISTATS 2016