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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書(shū)目名稱(chēng)Machine Learning and Knowledge Extraction影響因子(影響力)




書(shū)目名稱(chēng)Machine Learning and Knowledge Extraction影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Machine Learning and Knowledge Extraction網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




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書(shū)目名稱(chēng)Machine Learning and Knowledge Extraction讀者反饋學(xué)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

墻壁

Congestion

溝通

,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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籠子

,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