Beyond calibration: estimating the grouping loss of modern neural networks

28 October 2022

Papers citing "Beyond calibration: estimating the grouping loss of modern neural networks"

10 / 10 papers shown

Title
Rethinking Early Stopping: Refine, Then Calibrate Eugene Berta David Holzmüller Michael I. Jordan Francis Bach 72 0 0 31 Jan 2025
Reassessing How to Compare and Improve the Calibration of Machine Learning Models M. Chidambaram Rong Ge 74 1 0 06 Jun 2024
Revisiting Confidence Estimation: Towards Reliable Failure Prediction Fei Zhu Xu-Yao Zhang Zhen Cheng Cheng-Lin Liu UQCV 52 10 0 05 Mar 2024
On Calibration of Modern Quantized Efficient Neural Networks Joe-Hwa Kuang Alexander Wong UQCV MQ 27 1 0 25 Sep 2023
Beyond Confidence: Reliable Models Should Also Consider Atypicality Mert Yuksekgonul Linjun Zhang James Zou Carlos Guestrin 32 20 0 29 May 2023
Minimum-Risk Recalibration of Classifiers Zeyu Sun Dogyoon Song Alfred Hero 38 5 0 18 May 2023
How to select predictive models for causal inference? M. Doutreligne Gaël Varoquaux ELM CML 29 2 0 01 Feb 2023
Uncertainty Estimation based on Geometric Separation Gabriella Chouraqui L. Cohen Gil Einziger Liel Leman 33 0 0 11 Jan 2023
Calibrating AI Models for Wireless Communications via Conformal Prediction K. Cohen Sangwoo Park Osvaldo Simeone S. Shamai 32 6 0 15 Dec 2022
Metrics reloaded: Recommendations for image analysis validation Lena Maier-Hein Annika Reinke Patrick Godau M. Tizabi Florian Buettner ... Aleksei Tiulpin Sotirios A. Tsaftaris Ben Van Calster Gaël Varoquaux Paul F. Jäger 34 217 0 03 Jun 2022