arXiv Machine Learning
Let Confidence Change, Not the Prediction: Prediction-Preserving Repair for Post-hoc Calibration
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Aug 26
Focal Calibration Loss: Controlling Posterior Distortion in Deep Neural Classifiers
arXiv:2410.18321v3 Announce Type: replace Abstract: Confidence calibration matters wherever a classifier's probabilities, not just its labels, are consumed downstream. We study Focal Calibration Loss...
arXiv Machine Learning
Jun 19
Judging to Improve: A De-biased VLM-as-3D-Judge Protocol for Single-Image 3D Generation
arXiv:2606. 20364v1 Announce Type: new Abstract: A companion study established a de-biased, cross-model VLM-as-3D-judge that reliably ranks single-image-to-3D mesh quality where cheap geometry and CLIP proxies fall short.
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.
arXiv Computer Vision
Sep 10
How (Mis)calibrated is your Federated CLIP and what to do about it?
arXiv:2512.04305v3 Announce Type: replace Abstract: Vision-language models (VLMs) such as CLIP are increasingly adapted across decentralized data silos, yet the reliability of their predictions under...
Hugging Face Trending Papers
Jul 2
Rethinking Post-Hoc Calibration in Semantic Segmentation
Reliable confidence estimates are essential in semantic segmentation, especially in safety-critical settings where overconfident errors can mislead downstream decisions. Yet modern segmentation models often remain miscalibrated.
arXiv AI
Aug 5
LoCA: Forward-Only LLM Tuning after One-Shot Calibration with Local Credit Assignment
arXiv:2608. 03020v1 Announce Type: new Abstract: Parameter-efficient post-training reduces the number of trainable parameters, but still requires repeated end-to-end backpropagation through the frozen backbone.