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Open access Aug 2026

A Configurable Framework for Quantifying and Comparing Interpretability Across ML Models and Methods

This paper introduces a configurable framework of quantitative metrics to evaluate and rank interpretability across diverse ML models and interpretability techniques, enabling practitioners to systematically compare interpretability across a wide range of algorithms and techniques.

Batu Kaan Özen, T. Waschulzik, A. Knoll · 0 citations
Open access Sep 2026

Efficient and Robust Absolute Pose Estimation via Gravity-Prior-Driven Transformation Decoupling and Pose Refinement

A novel transformation strategy that exploits geometric relations derived from the gravity prior is proposed that achieves stronger performance compared to existing state-of-the-art (SOTA) approaches and is integrated into ORB-SLAM2.

Hu Cao, Qian-Yi Yang, Xin-Yi Li et al. · 0 citations

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