Hugging Face Trending Papers
Jun 29

Bandwidth Selection in Kernel Density Estimation for Model Calibration

As deep learning models are increasingly deployed in high-stakes applications, providing well-calibrated uncertainty estimates has become as critical as achieving high predictive accuracy. While Kernel Density Estimation (KDE) has emerged as a smooth and continuous alternative to traditional binning for quantifying miscalibration, its reliability is heavily dependent on the choice of the kernel bandwidth.

arXiv Computer Vision
Sep 21

Uncertainty-driven training for three-dimensional calibrated lung nodule classification

The paper introduces an uncertainty‑driven training framework for 3D CT lung nodule classification that uses validation‑based uncertainty estimates to reweight the loss, aiming to improve predictive performance and probability calibration. Two uncertainty quantification methods—Monte Carlo Dropout and Evidential Deep Learning—are evaluated across multiple backbone architectures (ResNet, DenseNet, EfficientNet, ViT, Swin) on the LIDC‑IDRI and NoduleMNIST3D datasets. The approach yields comparable classification accuracy to conventional training while substantially reducing expected calibration error, especially on convolutional backbones, and shows that simple temperature scaling can also achieve strong calibration.

By Giuseppe Tripodi, Alessandro De Rosis, Saleh Rezaeiravesh
arXiv Machine Learning
Aug 11

Dynamic Distribution-Aware Uncertainty Tracking in Vision-Language Representation Learning

arXiv:2608. 09011v1 Announce Type: new Abstract: Uncertainty Quantification (UQ) aims to measure the reliability of model predictions, serving as a critical safeguard for deploying Vision-Language Models (VLMs) in safety-critical scenarios.

By Ao Zhou, Zhiwei Jiang, Zifeng Cheng, Cong Wang, Shufan Yang, Haoru Chen, Qing Gu