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Titlebook: Engineering Applications of Neural Networks; 25th International C Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne Conference proceedings

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樓主: GLAZE
31#
發(fā)表于 2025-3-27 00:23:29 | 只看該作者
32#
發(fā)表于 2025-3-27 03:41:10 | 只看該作者
Micromachined Resonators and Circuits,hat with equal annotation effort aggregated uncertainties across image augmentations yield improved results compared to a baseline without augmentations, however certain configurations can be detrimental for the performance of the resulting model.
33#
發(fā)表于 2025-3-27 05:46:45 | 只看該作者
Review of Microinjection Systems,ills. This research contributes to our understanding of how practical LLMs are in real-world information extraction tasks and highlights the differences in performance among various state-of-the-art models.
34#
發(fā)表于 2025-3-27 12:33:57 | 只看該作者
https://doi.org/10.1007/978-1-4471-4597-4aller than that found in the selected traditional architectures for this study. It shows the potential of the Q-NAS algorithm and highlights the importance of efficient model design in the context of accurate and feature-aware medical image analysis.
35#
發(fā)表于 2025-3-27 17:11:03 | 只看該作者
James D. Lee,Jiaoyan Li,Zhen Zhang,Leyu Wangsion Trees emerged as the most effective, each achieving an accuracy of 82%. This study not only underscores the potential of machine learning in medical diagnostics but also paves the way for more accessible and efficient screening methods for neurodevelopmental disorders.
36#
發(fā)表于 2025-3-27 18:27:05 | 只看該作者
Active Learning with?Aggregated Uncertainties from?Image Augmentationshat with equal annotation effort aggregated uncertainties across image augmentations yield improved results compared to a baseline without augmentations, however certain configurations can be detrimental for the performance of the resulting model.
37#
發(fā)表于 2025-3-27 23:45:07 | 只看該作者
38#
發(fā)表于 2025-3-28 05:23:58 | 只看該作者
Comparative Study Between Q-NAS and?Traditional CNNs for?Brain Tumor Classificationaller than that found in the selected traditional architectures for this study. It shows the potential of the Q-NAS algorithm and highlights the importance of efficient model design in the context of accurate and feature-aware medical image analysis.
39#
發(fā)表于 2025-3-28 06:23:29 | 只看該作者
40#
發(fā)表于 2025-3-28 12:01:23 | 只看該作者
978-3-031-62494-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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