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Titlebook: Advances in Intelligent Data Analysis XXII; 22nd International S Ioanna Miliou,Nico Piatkowski,Panagiotis Papapetro Conference proceedings

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11#
發(fā)表于 2025-3-23 11:09:40 | 只看該作者
12#
發(fā)表于 2025-3-23 14:10:49 | 只看該作者
https://doi.org/10.1007/978-1-349-19471-1w GloNet’s capability to self-regulate, and its resilience to depth-related learning challenges, such as performance degradation. Our findings position GloNet as a viable alternative to traditional architectures like ResNets.
13#
發(fā)表于 2025-3-23 20:36:20 | 只看該作者
https://doi.org/10.1007/978-1-349-19471-1 careful attention to dataset properties when selecting a model for tabular data in machine learning – especially in an industrial setting, where larger and larger datasets with less and less carefully engineered features are becoming routinely available.
14#
發(fā)表于 2025-3-24 01:06:00 | 只看該作者
15#
發(fā)表于 2025-3-24 03:47:01 | 只看該作者
16#
發(fā)表于 2025-3-24 06:33:48 | 只看該作者
https://doi.org/10.1057/9780230604148rtainty in model predictions and ii) the discrimination error between training batches and subsequent test batches, serving as key indicators for identifying drift in AI model performance. We test our framework on simulated drift data where we can control the nature of change, and high-fidelity synt
17#
發(fā)表于 2025-3-24 11:27:32 | 只看該作者
Nathan T. Formaini D.O,Jonathan C. Levy M.D.ovide global explanations for the prediction of neural networks. The explanations provided allow the identification of the relationships that the network learned and can be used to identify possible errors during training. In this work, concept activation vectors and concept activation regions are u
18#
發(fā)表于 2025-3-24 15:44:48 | 只看該作者
Sternoclavicular Joint Injuries,mining models. We compare it against batch and incremental learners, including methods relying on active drift detection. Experiments with varied travel mode data sets representing both city and country levels show that the IEBSM method both detects drift in travel mode data and successfully adapts
19#
發(fā)表于 2025-3-24 19:07:57 | 只看該作者
Predicting Performance Drift in?AI Models of?Healthcare Without Ground Truth Labelskundung sozialer und gesellschaftlicher Bedingungen und Prozesse. Die Beitr?ge in diesem Buchnehmen Serien aus vielen verschiedenen Perspektiven in den Blick - von Psychologie, Medienwissenschaften, Amerikanistik, Kulturphilosophie bin hin zu Forensik und Neurobiologie.?.978-3-662-53689-6
20#
發(fā)表于 2025-3-25 01:40:27 | 只看該作者
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