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Gradient-free variational learning with conditional mixture networks

29 August 2024

Christopher L. Buckley

BDL

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Papers citing "Gradient-free variational learning with conditional mixture networks"

25 / 25 papers shown

Title
AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models Conor Heins Toon Van de Maele Alexander Tschantz Hampus Linander Dimitrije Marković ... Magnus T. Koudahl Marco Perin Karl J. Friston Tim Verbelen Christopher L. Buckley OCL 227 2 0 30 May 2025
Bayesian Predictive Coding Alexander Tschantz Magnus T. Koudahl Hampus Linander Lancelot Da Costa Conor Heins Jeff Beck Christopher L. Buckley BDL 303 1 0 31 Mar 2025
Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems Ran Wei Joseph Lee Shohei Wakayama Alexander Tschantz Conor Heins ... Mahault Albarracin Miguel de Prado Petter Horling Peter Winzell Renjith Rajagopal 254 4 0 14 Oct 2024
Laplace Redux -- Effortless Bayesian Deep LearningNeural Information Processing Systems (NeurIPS), 2021 Erik A. Daxberger Agustinus Kristiadi Alexander Immer Runa Eschenhagen Matthias Bauer Philipp Hennig BDL UQCV 659 384 0 28 Jun 2021
What Are Bayesian Neural Network Posteriors Really Like?International Conference on Machine Learning (ICML), 2021 Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 292 434 0 29 Apr 2021
Ultimate Pólya Gamma Samplers -- Efficient MCMC for possibly imbalanced binary and categorical dataJournal of the American Statistical Association (JASA), 2020 Gregor Zens Sylvia Fruhwirth-Schnatter Helga Wagner SyDa 386 16 0 13 Nov 2020
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients Juntang Zhuang Tommy M. Tang Yifan Ding S. Tatikonda Nicha Dvornek X. Papademetris James S. Duncan ODL 712 595 0 15 Oct 2020
Probabilistic Transformers J. Movellan Prasad Gabbur 152 2 0 15 Oct 2020
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting Adam D. Cobb Brian Jalaian BDL 194 82 0 14 Oct 2020
Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets Zhihui Shao Jianyi Yang Shaolei Ren OODD 179 11 0 16 Jun 2020
Data Augementation with Polya Inverse Gamma Jingyu He Nicholas G. Polson Jianeng Xu 257 5 0 29 May 2019
Conditionally conjugate mean-field variational Bayes for logistic models Daniele Durante T. Rigon 189 44 0 19 Nov 2017
Deep Gaussian Mixture Models C. Viroli Geoffrey J. McLachlan BDL 71 136 0 18 Nov 2017
On Calibration of Modern Neural NetworksInternational Conference on Machine Learning (ICML), 2017 Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 1.3K 6,792 0 14 Jun 2017
Composing graphical models with neural networks for structured representations and fast inference Matthew J. Johnson David Duvenaud Alexander B. Wiltschko S. R. Datta Ryan P. Adams BDL OCL 467 501 0 20 Mar 2016
A Universal Approximation Theorem for Mixture of Experts Models Hien Nguyen Luke R. Lloyd‐Jones Geoffrey J. McLachlan 156 45 0 11 Feb 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 1.4K 5,257 0 04 Jan 2016
Practical Bayesian model evaluation using leave-one-out cross-validation and WAICStatistics and computing (Stat. Comput.), 2015 Aki Vehtari Andrew Gelman Jonah Gabry 365 4,504 0 16 Jul 2015
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma Augmentation Scott W. Linderman Matthew J. Johnson Ryan P. Adams 159 103 0 18 Jun 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 370 989 0 18 Feb 2015
Black Box Variational InferenceInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2013 Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 501 1,210 0 31 Dec 2013
Learning Factored Representations in a Deep Mixture of ExpertsInternational Conference on Learning Representations (ICLR), 2013 David Eigen MarcÁurelio Ranzato Ilya Sutskever MoE 404 441 0 16 Dec 2013
Bayesian Hierarchical Mixtures of ExpertsConference on Uncertainty in Artificial Intelligence (UAI), 2002 Charles M. Bishop M. Svensén 176 169 0 19 Oct 2012
Stochastic Variational InferenceJournal of machine learning research (JMLR), 2012 Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 588 2,741 0 29 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte CarloJournal of machine learning research (JMLR), 2011 Matthew D. Hoffman Andrew Gelman 410 4,741 0 18 Nov 2011