Variational Inference

Perturbative Black Box Variational Inference NeurIPS 2017 Piecewise Latent Variables for Neural Variational Text Processing EMNLP 2017 Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space CVPR 2017 Prediction and Control with Temporal Segment Models ICML 2017 Random Feature Expansions for Deep Gaussian Processes ICML 2017 Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms AISTATS 2017 Scalable Variational Inference for Dynamical Systems NeurIPS 2017 Scalable Variational Inference for Super Resolution Microscopy AISTATS 2017 Sequence to Better Sequence: Continuous Revision of Combinatorial Structures ICML 2017 Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NeurIPS 2017 Stochastic Recurrent Neural Network for Speech Recognition Interspeech 2017 Tag-Aware Personalized Recommendation Using a Hybrid Deep Model IJCAI 2017 Tied Variational Autoencoder Backends for i-Vector Speaker Recognition Interspeech 2017 Toward Controlled Generation of Text ICML 2017 Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data NeurIPS 2017 VAE Learning via Stein Variational Gradient Descent NeurIPS 2017 Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold CVPR 2017 Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering IJCAI 2017 Variational Inference for Gaussian Process Models with Linear Complexity NeurIPS 2017 Variational Inference for Sparse and Undirected Models ICML 2017 Variational Inference via χ Upper Bound Minimization NeurIPS 2017 Variational Memory Addressing in Generative Models NeurIPS 2017 Variational Recurrent Neural Networks for Speech Separation Interspeech 2017 VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning NeurIPS 2017 Z-Forcing: Training Stochastic Recurrent Networks NeurIPS 2017