Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit

14 October 2020

Papers citing "Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit"

8 / 8 papers shown

Title
Uncertainty Quantification for Machine Learning in Healthcare: A Survey L. J. L. Lopez Shaza Elsharief Dhiyaa Al Jorf Firas Darwish Congbo Ma Farah E. Shamout 85 0 0 04 May 2025
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 15 4 0 15 May 2022
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes Jongseo Lee Jianxiang Feng Matthias Humt M. Müller Rudolph Triebel UQCV 46 21 0 20 Sep 2021
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen Roman Novak Lechao Xiao Jaehoon Lee DD 19 229 0 27 Jul 2021
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 156 234 0 04 Mar 2020
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 220 348 0 14 Jun 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,134 0 06 Jun 2015