Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks

27 October 2018

Papers citing "Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks"

6 / 6 papers shown

Title
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration Siguang Huang Yunli Wang Lili Mou Huayue Zhang Ziru Xu Chuan Yu Bo Zheng 73 15 0 13 Mar 2025
A Saliency-based Clustering Framework for Identifying Aberrant Predictions A. Tersol Montserrat Alexander R. Loftus Yael Daihes 12 0 0 11 Nov 2023
Uncertainty Estimation based on Geometric Separation Gabriella Chouraqui L. Cohen Gil Einziger Liel Leman 40 0 0 11 Jan 2023
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance? Christoph Koller Goran Kauermann Xiao Xiang Zhu UQCV 24 6 0 30 May 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 278 5,695 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015