Investigating the confidence calibratability of deep neural networks Investigating the confidence calibratability of deep neural networks

Authors

Deng-Bao Wang (ORCID: https://orcid.org/0000-0002-6130-7220)
Min-Ling Zhang (ORCID: https://orcid.org/0000-0003-1880-5918)

Publication Details

Journal: Scientia Sinica Informationis
Published: 2025-06-11
DOI: https://doi.org/10.1360/ssi-2024-0305
Primary Topic: Adversarial Robustness in Machine Learning
Type: article
Field-Weighted Citation Impact: 0.00

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article

Investigating the confidence calibratability of deep neural networks Investigating the confidence calibratability of deep neural networks

Deng-Bao Wang, Min-Ling Zhang

Jun 11, 2025

Scientia Sinica Informationis

Adversarial Robustness in Machine Learning

article

Investigating the confidence calibratability of deep neural networks Investigating the confidence calibratability of deep neural networks

Deng-Bao Wang, Min-Ling Zhang

June 11, 2025article en

Abstract

No abstract available for this paper.

Scientia Sinica InformationisVol. 55(9)

Openalex Percentile: Top 94%

Adversarial Robustness in Machine Learning

DOI Open Access PDF OpenAlex

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Investigating the confidence calibratability of deep neural networks Investigating the confidence calibratability of deep neural networks — Deng-Bao Wang, Min-Ling Zhang · Scientia Sinica Informationis (2025) | TGRS Research Map | TGRS