Stochastic Variational Inference

29 June 2012

Matt Hoffman

Papers citing "Stochastic Variational Inference"

50 / 1,065 papers shown

Title
Independent finite approximations for Bayesian nonparametric inference Tin D. Nguyen Jonathan H. Huggins L. Masoero Lester W. Mackey Tamara Broderick TPM 23 4 0 22 Sep 2020
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data Jonathan Pilault Amine Elhattami C. Pal CLL MoE 30 89 0 19 Sep 2020
Efficient Variational Bayes Learning of Graphical Models with Smooth Structural Changes Hang Yu Songwei Wu Justin Dauwels 21 5 0 16 Sep 2020
Fixed Inducing Points Online Bayesian Calibration for Computer Models with an Application to a Scale-Resolving CFD Simulation Y. Duan M. Eaton Michael Bluck 6 4 0 15 Sep 2020
Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting Amirreza Farnoosh Bahar Azari Sarah Ostadabbas BDL AI4TS 18 20 0 10 Sep 2020
Generalized Multi-Output Gaussian Process Censored Regression Daniele Gammelli Kasper Pryds Rolsted Dario Pacino Filipe Rodrigues 22 14 0 10 Sep 2020
Online Estimation and Community Detection of Network Point Processes for Event Streams Guanhua Fang Owen G. Ward Tian Zheng 4 3 0 03 Sep 2020
Robust, Accurate Stochastic Optimization for Variational Inference Akash Kumar Dhaka Alejandro Catalina Michael Riis Andersen Maans Magnusson Jonathan H. Huggins Aki Vehtari 30 33 0 01 Sep 2020
$β$ -Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers Dionysis Manousakas Cecilia Mascolo 25 2 0 31 Aug 2020
Locally induced Gaussian processes for large-scale simulation experiments D. Cole R. Christianson R. Gramacy 21 21 0 28 Aug 2020
Channel-Directed Gradients for Optimization of Convolutional Neural Networks Dong Lao Peihao Zhu Peter Wonka G. Sundaramoorthi 40 3 0 25 Aug 2020
An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks Anika Tabassum N. Khan UQCV 12 1 0 12 Aug 2020
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach M. Tadayon G. Pottie BDL AI4TS 7 5 0 09 Aug 2020
Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning Tristan Miller E. Dinh Edwin Simpson Iryna Gurevych 20 5 0 03 Aug 2020
Variational approximations of empirical Bayes posteriors in high-dimensional linear models Yue Yang Ryan Martin 28 7 0 31 Jul 2020
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks Alexander Immer BDL 19 4 0 21 Jul 2020
Semi Conditional Variational Auto-Encoder for Flow Reconstruction and Uncertainty Quantification from Limited Observations K. Gundersen A. Oleynik N. Blaser G. Alendal BDL 30 28 0 19 Jul 2020
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users Laurent Valentin Jospin Wray Buntine F. Boussaïd Hamid Laga Bennamoun OOD BDL UQCV 29 611 0 14 Jul 2020
Control as Hybrid Inference Alexander Tschantz Beren Millidge A. Seth Christopher L. Buckley 21 9 0 11 Jul 2020
Training Restricted Boltzmann Machines with Binary Synapses using the Bayesian Learning Rule Xiangming Meng 27 0 0 09 Jul 2020
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks Meet P. Vadera Adam D. Cobb B. Jalaeian Benjamin M. Marlin BDL UQCV 27 16 0 08 Jul 2020
Fast Bayesian Estimation of Spatial Count Data Models P. Bansal Rico Krueger D. Graham 31 8 0 07 Jul 2020
Stochastic Variational Bayesian Inference for a Nonlinear Forward Model M. Chappell A. Groves M. Woolrich 11 353 0 03 Jul 2020
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective Jacky Y. Zhang Rekha Khanna Anastasios Kyrillidis Oluwasanmi Koyejo 14 0 0 01 Jul 2020
Learning Sparse Prototypes for Text Generation Junxian He Taylor Berg-Kirkpatrick Graham Neubig 27 23 0 29 Jun 2020
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation Victor Picheny Vincent Dutordoir A. Artemev N. Durrande 19 2 0 25 Jun 2020
Stratified stochastic variational inference for high-dimensional network factor model E. Aliverti Massimiliano Russo BDL 10 8 0 25 Jun 2020
Normalizing Flows Across Dimensions Edmond Cunningham Renos Zabounidis Abhinav Agrawal Ina Fiterau Daniel Sheldon DRL 14 26 0 23 Jun 2020
Bayesian Neural Networks: An Introduction and Survey Ethan Goan Clinton Fookes BDL UQCV 37 199 0 22 Jun 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle Jacob R. Gardner 21 43 0 19 Jun 2020
Distortion estimates for approximate Bayesian inference Hanwen Xing Geoff K. Nicholls J. Lee 16 7 0 19 Jun 2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal Daniel Sheldon Justin Domke TPM BDL 19 38 0 18 Jun 2020
Analytical Probability Distributions and EM-Learning for Deep Generative Networks Randall Balestriero Sébastien Paris Richard G. Baraniuk UQCV DRL 16 1 0 17 Jun 2020
On the Variational Posterior of Dirichlet Process Deep Latent Gaussian Mixture Models Amine Echraibi Joachim Flocon-Cholet Stéphane Gosselin Sandrine Vaton BDL 6 4 0 16 Jun 2020
PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons Xinjie Lan Xin Guo Kenneth Barner 19 3 0 16 Jun 2020
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference Hao Zhang Bo Chen Yulai Cong D. Guo Hongwei Liu Mingyuan Zhou BDL 35 27 0 15 Jun 2020
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder Nicholas Gao M. Wilson T. Vandal W. Vinci R. Nemani E. Rieffel DRL 17 19 0 13 Jun 2020
Reinforcement Learning as Iterative and Amortised Inference Beren Millidge Alexander Tschantz A. Seth Christopher L. Buckley OffRL 18 3 0 13 Jun 2020
Approximate Inference for Spectral Mixture Kernel Yohan Jung Kyungwoo Song Jinkyoo Park BDL 6 2 0 12 Jun 2020
Multi-index Antithetic Stochastic Gradient Algorithm Mateusz B. Majka Marc Sabate Vidales Łukasz Szpruch 37 0 0 10 Jun 2020
Variational Auto-Regressive Gaussian Processes for Continual Learning Sanyam Kapoor Theofanis Karaletsos T. Bui BDL 19 24 0 09 Jun 2020
Variational Model-based Policy Optimization Yinlam Chow Brandon Cui Moonkyung Ryu Mohammad Ghavamzadeh OffRL 22 12 0 09 Jun 2020
A Survey of Bayesian Statistical Approaches for Big Data Farzana Jahan Insha Ullah Kerrie Mengersen 44 14 0 08 Jun 2020
Bayesian Neural Network via Stochastic Gradient Descent Abhinav Sagar UQCV BDL 18 2 0 04 Jun 2020
Quadruply Stochastic Gaussian Processes Trefor W. Evans P. Nair GP 9 3 0 04 Jun 2020
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables Maximilian Rixner P. Koutsourelakis AI4CE 13 18 0 02 Jun 2020
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study Himanshu Sharma Elise Jennings BDL 27 3 0 23 May 2020
Correlated Mixed Membership Modeling of Somatic Mutations Rahul Mehta M. Karaman 6 0 0 21 May 2020
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation Bryan Eikema Wilker Aziz 16 131 0 20 May 2020
Infinite-dimensional gradient-based descent for alpha-divergence minimisation Kamélia Daudel Randal Douc Franccois Portier 21 17 0 20 May 2020