variational inference

Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server JMLR 2017 Doubly Stochastic Variational Inference for Deep Gaussian Processes NeurIPS 2017 Dropout Inference in Bayesian Neural Networks with Alpha-divergences ICML 2017 Dynamic Collaborative Filtering With Compound Poisson Factorization AISTATS 2017 Dynamic Word Embeddings ICML 2017 Efficient and Flexible Inference for Stochastic Systems NeurIPS 2017 Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes NeurIPS 2017 Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models NeurIPS 2017 Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems NeurIPS 2017 Fast amortized inference of neural activity from calcium imaging data with variational autoencoders NeurIPS 2017 Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets AISTATS 2017 Fast Black-box Variational Inference through Stochastic Trust-Region Optimization NeurIPS 2017 f-GANs in an Information Geometric Nutshell NeurIPS 2017 Filtering Variational Objectives NeurIPS 2017 Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo ICML 2017 From Bayesian Sparsity to Gated Recurrent Nets NeurIPS 2017 From Patches to Images: A Nonparametric Generative Model ICML 2017 G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition CVPR 2017 Gauging Variational Inference NeurIPS 2017 GPflow: A Gaussian Process Library using TensorFlow JMLR 2017 Gray-box Inference for Structured Gaussian Process Models AISTATS 2017 Hierarchically-partitioned Gaussian Process Approximation AISTATS 2017 Identification of Gaussian Process State Space Models NeurIPS 2017 Improving Variational Methods via Pairwise Linear Response Identities JMLR 2017 Inference Compilation and Universal Probabilistic Programming AISTATS 2017