arXiv Machine Learning July 10, 2026 By Arthur Chiron (IRIT, EPE UT), Franck Mamalet (IRIT, DTIPG - SNCF, UT3), Thomas Massena (IRIT, DTIPG - SNCF, UT3), Thomas Deltort (IRIT), Mathieu Serrurier (IRIT, UT2J)

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2607. 07745v1 Announce Type: new Abstract: While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneously remains a central challenge.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

Jun 30

FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibration

arXiv:2606. 28654v1 Announce Type: cross Abstract: Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods.

By Thiru Thillai Nadarasar Bahavan, Sachith Seneviratne, Saman Halgamuge

benchmarks

arXiv Machine Learning

Aug 12

Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration

arXiv:2608. 10372v1 Announce Type: new Abstract: Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining.

By Lening Zhao, Qipeng Zhan, Li Shen

computer-vision benchmarks

arXiv AI

Jun 2

CalArena: A Large-Scale Post-Hoc Calibration Benchmark

arXiv:2605. 30188v2 Announce Type: replace-cross Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated.

By Eug\`ene Berta, David Holzm\"uller, Francis Bach, Michael I. Jordan

computer-vision benchmarks

arXiv Machine Learning

Jul 14

When does distribution shift break graph neural networks calibration?

arXiv:2607. 10804v1 Announce Type: new Abstract: Graph neural networks (GNNs) are increasingly deployed in real-world applications where distribution shift is un-avoidable.

By Abderaouf Bahi

benchmarks

arXiv Machine Learning

Jun 25

Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization

arXiv:2505. 23866v2 Announce Type: replace Abstract: Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving.

By Chengli Tan, Yubo Zhou, Haishan Ye, Guang Dai, Junmin Liu, Zengjie Song, Jiangshe Zhang, Zixiang Zhao, Yunda Hao, Yong Xu

agents robotics safety

arXiv Machine Learning

Jul 3

Rethinking Post-Hoc Calibration in Semantic Segmentation

arXiv:2607. 01902v1 Announce Type: cross Abstract: Reliable confidence estimates are essential in semantic segmentation, especially in safety-critical settings where overconfident errors can mislead downstream decisions.

By Tristan Kirscher (ICube), Kim-Celine Kahl (DKFZ), Balint Kovacs (DKFZ), Maximilian R. Rokuss (DKFZ), Klaus Maier-Hein (DKFZ), Xavier Coubez (ICube), Philippe Meyer (ICube), Sylvain Faisan (ICube)

computer-vision benchmarks safety

Related stories

FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibration

Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration

CalArena: A Large-Scale Post-Hoc Calibration Benchmark

When does distribution shift break graph neural networks calibration?

Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization

Rethinking Post-Hoc Calibration in Semantic Segmentation