Titlebook: Machine Learning and Knowledge Extraction; 7th IFIP TC 5, TC 12 Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2023

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书目名称Machine Learning and Knowledge Extraction影响因子(影响力)




书目名称Machine Learning and Knowledge Extraction影响因子(影响力)学科排名




书目名称Machine Learning and Knowledge Extraction网络公开度




书目名称Machine Learning and Knowledge Extraction网络公开度学科排名




书目名称Machine Learning and Knowledge Extraction被引频次




书目名称Machine Learning and Knowledge Extraction被引频次学科排名




书目名称Machine Learning and Knowledge Extraction年度引用




书目名称Machine Learning and Knowledge Extraction年度引用学科排名




书目名称Machine Learning and Knowledge Extraction读者反馈




书目名称Machine Learning and Knowledge Extraction读者反馈学科排名

Anticoagulant

,Domain-Specific Evaluation of Visual Explanations for Application-Grounded Facial Expression Recognshow that the domain-specific evaluation is especially beneficial for challenging use cases such as facial expression recognition and provides application-grounded quality criteria that are not covered by standard evaluation methods. Our comparison of the domain-specific evaluation method with stand

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,Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning,icial Intelligence (xAI) methods, changing the datasets to create counterfactual explanations. The adapted datasets could influence the local model’s characteristics and thereby create a federated version that distils their diverse knowledge in a centralized scenario. This work demonstrates the feas

MAZE

Serenity

,Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data,yper-Stacked, a novel AutoML component built natively on Apache Spark. Hyper-Stacked combines multi-fidelity hyperparameter optimisation with the Super Learner stacking technique to produce a strong and diverse ensemble. Integration with Spark allows for a parallelised and distributed approach, capa

墙壁

Congestion

沟通

,Let Me Think! Investigating the Effect of Explanations Feeding Doubts About the AI Advice,h pixel attribution maps. These cases were associated with the same AI advice for the base case, but one case was accurate while the other was erroneous with respect to the ground truth. While the introduction of this support system did not significantly enhance diagnostic accuracy, it was highly va

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笼子

,The Split Matters: Flat Minima Methods for Improving the Performance of GNNs, can improve the performance of GNN models by over 2 points, if the train-test split is randomized. Following Shchur et al., randomized splits are essential for a fair evaluation of GNNs, as other (fixed) splits like “Planetoid” are biased. Overall, we provide important insights for improving and fa