Madison
发表于 2025-3-21 17:12:47
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Anticoagulant
发表于 2025-3-22 00:07:42
,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
NOT
发表于 2025-3-22 04:15:06
,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
发表于 2025-3-22 07:59:39
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Serenity
发表于 2025-3-22 11:10:51
,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
发表于 2025-3-22 14:41:09
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Congestion
发表于 2025-3-22 17:09:49
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沟通
发表于 2025-3-23 00:29:15
,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
不透气
发表于 2025-3-23 03:18:48
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笼子
发表于 2025-3-23 08:14:59
,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