确保
发表于 2025-3-27 00:23:29
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合适
发表于 2025-3-27 03:41:10
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.
Vldl379
发表于 2025-3-27 05:46:45
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.
CHAFE
发表于 2025-3-27 12:33:57
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.
MIR
发表于 2025-3-27 17:11:03
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.
Dawdle
发表于 2025-3-27 18:27:05
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.
拍下盗公款
发表于 2025-3-27 23:45:07
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基因组
发表于 2025-3-28 05:23:58
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.
lethal
发表于 2025-3-28 06:23:29
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osteopath
发表于 2025-3-28 12:01:23
978-3-031-62494-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl