Black Box Variational Inference

31 December 2013

Papers citing "Black Box Variational Inference"

26 / 26 papers shown

Title
Discrete Neural Flow Samplers with Locally Equivariant Transformer Zijing Ou Ruixiang Zhang Yingzhen Li 46 0 0 23 May 2025
Latent Video Dataset Distillation Ning Li Antai Andy Liu Jingran Zhang Justin Cui DD VGen 104 0 0 23 Apr 2025
Uncertainty quantification of neural network models of evolving processes via Langevin sampling Cosmin Safta Reese E. Jones Ravi G. Patel Raelynn Wonnacot Dan S. Bolintineanu Craig M. Hamel S. Kramer BDL 86 0 0 21 Apr 2025
ELBOing Stein: Variational Bayes with Stein Mixture Inference Ola Rønning Eric T. Nalisnick Christophe Ley Padhraic Smyth Thomas Hamelryck BDL 81 1 0 30 Oct 2024
Batch, match, and patch: low-rank approximations for score-based variational inference Chirag Modi Diana Cai Lawrence K. Saul BDL 68 1 0 29 Oct 2024
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix C. Margossian Lawrence K. Saul 68 2 0 14 Oct 2024
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold Hoang Phuc Hau Luu Hanlin Yu Bernardo Williams Marcelo Hartmann Arto Klami DRL 106 0 0 03 Oct 2024
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling Alex Glyn-Davies Arnaud Vadeboncoeur O. Deniz Akyildiz Ieva Kazlauskaite Mark Girolami PINN 82 0 0 10 Sep 2024
Variational Search Distributions Daniel M. Steinberg Rafael Oliveira Cheng Soon Ong Edwin V. Bonilla 57 1 0 10 Sep 2024
Gradient-free variational learning with conditional mixture networks Conor Heins Hao Wu Dimitrije Marković Alexander Tschantz Jeff Beck Christopher L. Buckley BDL 63 3 0 29 Aug 2024
SoftCVI: Contrastive variational inference with self-generated soft labels Daniel Ward Mark Beaumont Matteo Fasiolo BDL 151 0 0 22 Jul 2024
Demystifying SGD with Doubly Stochastic Gradients Kyurae Kim Joohwan Ko Yian Ma Jacob R. Gardner 81 1 0 03 Jun 2024
LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks Michelle Halbheer Dominik J. Mühlematter Alexander Becker Dominik Narnhofer Helge Aasen Konrad Schindler Mehmet Özgür Türkoglu UQCV 72 2 0 23 May 2024
Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem Roumen Nikolaev Popov 66 0 0 16 Apr 2024
Improved off-policy training of diffusion samplers Marcin Sendera Minsu Kim Sarthak Mittal Pablo Lemos Luca Scimeca Jarrid Rector-Brooks Alexandre Adam Yoshua Bengio Nikolay Malkin OffRL 100 24 0 07 Feb 2024
The VampPrior Mixture Model Andrew Stirn David A. Knowles BDL 62 1 0 06 Feb 2024
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference Dai Hai Nguyen Tetsuya Sakurai Hiroshi Mamitsuka 67 1 0 25 Oct 2023
Deep Amortized Inference for Probabilistic Programs Daniel E. Ritchie Paul Horsfall Noah D. Goodman TPM 88 82 0 18 Oct 2016
Variational Inference for Sparse and Undirected Models John Ingraham D. Marks 64 8 0 11 Feb 2016
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server Leonard Hasenclever Stefan Webb Thibaut Lienart Sebastian J. Vollmer Balaji Lakshminarayanan Charles Blundell Yee Whye Teh BDL 108 70 0 31 Dec 2015
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 406 16,947 0 20 Dec 2013
Automated Variational Inference in Probabilistic Programming David Wingate T. Weber BDL TPM 85 138 0 07 Jan 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 230 2,619 0 29 Jun 2012
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression Tim Salimans David A. Knowles 110 251 0 28 Jun 2012
Variational Bayesian Inference with Stochastic Search John Paisley David M. Blei Michael I. Jordan BDL 104 499 0 27 Jun 2012
Variational inference for large-scale models of discrete choice Michael Braun Jon D. McAuliffe 85 186 0 15 Dec 2007