37 citations · 78 across the 32 of their papers we have counts for
9 papers · 1 filter
Confidence Calibration under Ambiguous Ground Truth
Linwei Tao, Haoyang Luo, Minjing Dong +1
Confidence calibration assumes a unique ground-truth label per input, yet this assumption fails wherever annotators genuinely disagree. Post-hoc calibrators fitted on majority-vote…
WATS: Calibrating Graph Neural Networks with Wavelet-Aware Temperature Scaling
Xiaoyang Li, Linwei Tao, Haohui Lu +3
Graph Neural Networks (GNNs) have demonstrated strong predictive performance on relational data; however, their confidence estimates often misalign with actual predictive correctne…
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…
Uncertainty Weighted Gradients for Model Calibration
Jinxu Lin, Linwei Tao, Minjing Dong +1
Model calibration is essential for ensuring that the predictions of deep neural networks accurately reflect true probabilities in real-world classification tasks. However, deep net…
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…
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models
Jinxu Lin, Linwei Tao, Minjing Dong +1
As diffusion models become increasingly popular, the misuse of copyrighted and private images has emerged as a major concern. One promising solution to mitigate this issue is ident…
Imitation Learning from Purified Demonstrations
Yunke Wang, Minjing Dong, Yukun Zhao +2
Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in r…
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…
Calibrating a Deep Neural Network with Its Predecessors
Linwei Tao, Minjing Dong, Daochang Liu +2
Confidence calibration - the process to calibrate the output probability distribution of neural networks - is essential for safety-critical applications of such networks. Recent wo…