activity
20232026
most citedA Benchmark Study on Calibration

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

MetaRefine: Score Refinement with Multi-Level Features for Image Assessment

Chuanzhi Xu, Chengkun Yue, Haolan Guo +5

An image's original dimensions, aspect ratio, and composition provide important cues that can affect its technical and aesthetic quality. Image assessment methods use resizing, cro…

cs.RO2026

Prepare to Turn: Learning Anticipatory Maneuvering for Fast Sequential Quadrupedal Navigation

Zunzhi You, Yunke Wang, Haolan Guo +1

Fast quadrupedal navigation requires preparing for the next turn while approaching the current goal. Policies trained to reach and remain at a single target can instead produce sto…

cs.LG2025

Sample Margin-Aware Recalibration of Temperature Scaling

Haolan Guo, Linwei Tao, Haoyang Luo +2

Recent advances in deep learning have significantly improved predictive accuracy. However, modern neural networks remain systematically overconfident, posing risks for deployment i…

cs.LG2024

Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors

Linwei Tao, Haolan Guo, Minjing Dong +1

Calibration is crucial in deep learning applications, especially in fields like healthcare and autonomous driving, where accurate confidence estimates are vital for decision-making…

cs.LG2023★ 1 cited

A Benchmark Study on Calibration

Linwei Tao, Younan Zhu, Haolan Guo +2

Deep neural networks are increasingly utilized in various machine learning tasks. However, as these models grow in complexity, they often face calibration issues, despite enhanced…