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Titlebook: Artificial Intelligence. ECAI 2023 International Workshops; XAI^3, TACTIFUL, XI- S?awomir Nowaczyk,Przemys?aw Biecek,Vania Dimitrov Confere

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21#
發(fā)表于 2025-3-25 06:50:23 | 只看該作者
22#
發(fā)表于 2025-3-25 11:31:19 | 只看該作者
Achieving Complete Coverage with?Hypercube-Based Symbolic Knowledge-Extraction Techniques augment the predictive capabilities of hypercube-based SKE techniques, striving for a completeness rate of 100%. Furthermore, the study includes experiments that assess the effectiveness of the proposed enhancements.
23#
發(fā)表于 2025-3-25 12:45:35 | 只看該作者
Leveraging Model-Based Trees as?Interpretable Surrogate Models for?Model Distillation ability to generate such surrogate models. We investigate fidelity, interpretability, stability, and the algorithms’ capability to capture interaction effects through appropriate splits. Based on our comprehensive analyses, we finally provide an overview of user-specific recommendations.
24#
發(fā)表于 2025-3-25 18:10:57 | 只看該作者
25#
發(fā)表于 2025-3-25 20:28:09 | 只看該作者
26#
發(fā)表于 2025-3-26 02:44:52 | 只看該作者
27#
發(fā)表于 2025-3-26 05:49:47 | 只看該作者
https://doi.org/10.1007/978-1-4613-1173-7te the approach’s effectiveness on real-world driving data and demonstrate its ability to identify critical driving situations successfully. Moreover, we discuss the challenges associated with the approach and outline future research activities.
28#
發(fā)表于 2025-3-26 08:32:40 | 只看該作者
29#
發(fā)表于 2025-3-26 12:39:27 | 只看該作者
30#
發(fā)表于 2025-3-26 18:16:07 | 只看該作者
Identifying Critical Scenarios in?Autonomous Driving During Operationte the approach’s effectiveness on real-world driving data and demonstrate its ability to identify critical driving situations successfully. Moreover, we discuss the challenges associated with the approach and outline future research activities.
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