variational inference

An Empirical Bayes Approach to Optimizing Machine Learning Algorithms NeurIPS 2017 A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting CVPR 2017 A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks NeurIPS 2017 Asynchronous Distributed Variational Gaussian Process for Regression ICML 2017 Asynchronous Temporal Fields for Action Recognition CVPR 2017 Attributing Hacks AISTATS 2017 A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation JMLR 2017 Automatic Differentiation Variational Inference JMLR 2017 Averaged Collapsed Variational Bayes Inference JMLR 2017 Bayesian Eigenobjects: A Unified Framework for 3D Robot Perception RSS 2017 Bayesian Hilbert Maps for Dynamic Continuous Occupancy Mapping CoRL 2017 Bayesian inference on random simple graphs with power law degree distributions ICML 2017 Bayesian Models of Data Streams with Hierarchical Power Priors ICML 2017 Co-clustering through Optimal Transport ICML 2017 Collapsed variational Bayes for Markov jump processes NeurIPS 2017 Concrete Dropout NeurIPS 2017 Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models AISTATS 2017 Context Selection for Embedding Models NeurIPS 2017 Convolutional Gaussian Processes NeurIPS 2017 Deep Dynamic Poisson Factorization Model NeurIPS 2017 Deep Generative Models for Relational Data with Side Information ICML 2017 Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks NeurIPS 2017 Deep Recurrent Generative Decoder for Abstractive Text Summarization EMNLP 2017 Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA JMLR 2017 Differentiable Learning of Submodular Models NeurIPS 2017