Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes

17 May 2020

Papers citing "Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes"

14 / 14 papers shown

Title
Sparse Gaussian Neural Processes Tommy Rochussen Vincent Fortuin BDL UQCV 58 0 0 02 Apr 2025
Are you using test log-likelihood correctly? Sameer K. Deshpande Soumya K. Ghosh Tin D. Nguyen Tamara Broderick 27 7 0 01 Dec 2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antorán David Janz J. Allingham Erik A. Daxberger Riccardo Barbano Eric T. Nalisnick José Miguel Hernández-Lobato UQCV BDL 27 28 0 17 Jun 2022
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Jiri Hron Roman Novak Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 42 6 0 15 Jun 2022
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility Hoileong Lee Fadhel Ayed Paul Jung Juho Lee Hongseok Yang François Caron 40 10 0 17 May 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 31 44 0 22 Feb 2022
Gradient Descent on Neurons and its Link to Approximate Second-Order Optimization Frederik Benzing ODL 37 23 0 28 Jan 2022
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 19 4 0 01 Oct 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 26 24 0 11 Jun 2021
Data augmentation in Bayesian neural networks and the cold posterior effect Seth Nabarro Stoil Ganev Adrià Garriga-Alonso Vincent Fortuin Mark van der Wilk Laurence Aitchison BDL 21 37 0 10 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 29 124 0 14 May 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 21 107 0 24 Feb 2021
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 175 51 0 17 Oct 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015