Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes

19 July 2017

Papers citing "Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes"

11 / 11 papers shown

Title
SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes Nima Negarandeh Carlos Mora Ramin Bostanabad 55 0 0 18 Mar 2025
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling Jasmine Bayrooti Carl Henrik Ek Amanda Prorok 42 0 0 07 Oct 2024
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 60 56 0 23 Feb 2022
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 33 124 0 14 May 2021
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series Xing Han S. Dasgupta Joydeep Ghosh AI4TS 33 34 0 25 Feb 2021
Few-shot Learning for Spatial Regression Tomoharu Iwata Yusuke Tanaka 30 11 0 09 Oct 2020
Efficient Transfer Bayesian Optimization with Auxiliary Information Tomoharu Iwata Takuma Otsuka 17 2 0 17 Sep 2019
Meta-Learning Mean Functions for Gaussian Processes Vincent Fortuin Heiko Strathmann Gunnar Rätsch BDL FedML MLT 24 29 0 23 Jan 2019
Variational Implicit Processes Chao Ma Yingzhen Li José Miguel Hernández-Lobato BDL 24 68 0 06 Jun 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 92 271 0 24 Feb 2014