Deep Neural Networks as Point Estimates for Deep Gaussian Processes

10 May 2021

Papers citing "Deep Neural Networks as Point Estimates for Deep Gaussian Processes"

11 / 11 papers shown

Title
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models Bo-wen Li Alexandar J. Thomson Matthew M. Engelhard David Page David Page BDL AI4CE 24 0 0 27 May 2023
Gaussian Process-Gated Hierarchical Mixtures of Experts Yuhao Liu Marzieh Ajirak P. Djuric MoE 16 1 0 09 Feb 2023
The Unreasonable Effectiveness of Deep Evidential Regression N. Meinert J. Gawlikowski Alexander Lavin UQCV EDL 182 35 0 20 May 2022
Sharp asymptotics on the compression of two-layer neural networks Mohammad Hossein Amani Simone Bombari Marco Mondelli Rattana Pukdee Stefano Rini MLT 27 0 0 17 May 2022
GPEX, A Framework For Interpreting Artificial Neural Networks Amir Akbarnejad G. Bigras Nilanjan Ray 47 4 0 18 Dec 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 4 0 01 Oct 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 0 11 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 31 124 0 14 May 2021
Multivariate Deep Evidential Regression N. Meinert Alexander Lavin BDL PER EDL UQCV 35 21 0 13 Apr 2021
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 40 94 0 02 Mar 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015