Explainable Artificial Intelligence Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part II /
This four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024. The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers a...
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| Drugi avtorji: | , , |
| Format: | Elektronski eKnjiga |
| Jezik: | angleščina |
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Springer Nature Switzerland : Imprint: Springer,
2024.
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| Izdaja: | 1st ed. 2024. |
| Serija: | Communications in Computer and Information Science,
2154 |
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| Online dostop: | https://doi.org/10.1007/978-3-031-63797-1 |
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- -- XAI for graphs and Computer vision.
- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems.
- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study.
- Explainable AI for Mixed Data Clustering.
- Explaining graph classifiers by unsupervised node relevance attribution.
- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention.
- Graph Edits for Counterfactual Explanations: A comparative study.
- Model guidance via explanations turns image classifiers into segmentation models.
- Understanding the Dependence of Perception Model Competency on Regions in an Image.
- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation.
- Explainable Emotion Decoding for Human and Computer Vision.
- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.
- Logic, reasoning, and rule-based explainable AI.
- Template Decision Diagrams for Meta Control and Explainability.
- A Logic of Weighted Reasons for Explainable Inference in AI.
- On Explaining and Reasoning about Fiber Optical Link Problems.
- Construction of artificial most representative trees by minimizing tree-based distance measures.
- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles.
- Model-agnostic and statistical methods for eXplainable AI.
- Observation-specific explanations through scattered data approximation.
- CNN-based explanation ensembling for dataset, representation and explanations evaluation.
- Local List-wise Explanations of LambdaMART.
- Sparseness-Optimized Feature Importance.
- Stabilizing Estimates of Shapley Values with Control Variates.
- A Guide to Feature Importance Methods for Scientific Inference.
- Interpretable Machine Learning for TabPFN.
- Statistics and explainability: a fruitful alliance.
- How Much Can Stratification Improve the Approximation of Shapley Values?.