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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"

24 / 24 papers shown

Title
Bayesian Predictive Coding Alexander Tschantz Magnus T. Koudahl Hampus Linander Lancelot Da Costa Conor Heins Jeff Beck Christopher L. Buckley BDL 111 0 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 75 3 0 14 Oct 2024
Laplace Redux -- Effortless Bayesian Deep Learning Erik A. Daxberger Agustinus Kristiadi Alexander Immer Runa Eschenhagen Matthias Bauer Philipp Hennig BDL UQCV 221 315 0 28 Jun 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 74 387 0 29 Apr 2021
Ultimate Pólya Gamma Samplers -- Efficient MCMC for possibly imbalanced binary and categorical data Gregor Zens Sylvia Fruhwirth-Schnatter Helga Wagner SyDa 95 13 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 182 518 0 15 Oct 2020
Probabilistic Transformers J. Movellan Prasad Gabbur 28 2 0 15 Oct 2020
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting Adam D. Cobb Brian Jalaian BDL 74 76 0 14 Oct 2020
Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets Zhihui Shao Jianyi Yang Shaolei Ren OODD 67 11 0 16 Jun 2020
Data Augementation with Polya Inverse Gamma Jingyu He Nicholas G. Polson Jianeng Xu 62 5 0 29 May 2019
Conditionally conjugate mean-field variational Bayes for logistic models Daniele Durante T. Rigon 47 37 0 19 Nov 2017
Deep Gaussian Mixture Models C. Viroli Geoffrey J. McLachlan BDL 44 115 0 18 Nov 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,871 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 92 485 0 20 Mar 2016
A Universal Approximation Theorem for Mixture of Experts Models Hien Nguyen Luke R. Lloyd‐Jones Geoffrey J. McLachlan 57 41 0 11 Feb 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 298 4,812 0 04 Jan 2016
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC Aki Vehtari Andrew Gelman Jonah Gabry 124 4,060 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 55 98 0 18 Jun 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 142 945 0 18 Feb 2015
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 150 1,167 0 31 Dec 2013
Learning Factored Representations in a Deep Mixture of Experts David Eigen MarcÁurelio Ranzato Ilya Sutskever MoE 94 377 0 16 Dec 2013
Bayesian Hierarchical Mixtures of Experts Charles M. Bishop M. Svensén 83 167 0 19 Oct 2012
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 262 2,627 0 29 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 180 4,313 0 18 Nov 2011