A variational approximate posterior for the deep Wishart process

21 July 2021

Papers citing "A variational approximate posterior for the deep Wishart process"

8 / 8 papers shown

Title
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines Edward Milsom Ben Anson Laurence Aitchison 28 0 0 08 Oct 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel ... David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang UQCV BDL 55 27 0 01 Feb 2024
Convolutional Deep Kernel Machines Edward Milsom Ben Anson Laurence Aitchison BDL 26 5 0 18 Sep 2023
An Improved Variational Approximate Posterior for the Deep Wishart Process Sebastian W. Ober Ben Anson Edward Milsom Laurence Aitchison BDL 31 5 0 23 May 2023
Guided Deep Kernel Learning Idan Achituve Gal Chechik Ethan Fetaya BDL 31 5 0 19 Feb 2023
A theory of representation learning gives a deep generalisation of kernel methods Adam X. Yang Maxime Robeyns Edward Milsom Ben Anson Nandi Schoots Laurence Aitchison BDL 32 10 0 30 Aug 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 287 9,156 0 06 Jun 2015