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Titlebook: Computing and Data Science; Third International Weijia Cao,Aydogan Ozcan,Bei Guan Conference proceedings 2021 Springer Nature Singapore Pt

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41#
發(fā)表于 2025-3-28 16:00:01 | 只看該作者
Polyp Segmentation Using Fully Convolutional Neural Network with Dropout and CBAMimization techniques: convoluted block attention module and dropout. We conducted and evaluated experiments with performance metrics and concluded that convoluted block attention module and dropout have positive influence on the model, and our optimized model has advantage over some state-of-art models.
42#
發(fā)表于 2025-3-28 21:39:29 | 只看該作者
43#
發(fā)表于 2025-3-29 02:00:26 | 只看該作者
44#
發(fā)表于 2025-3-29 06:22:09 | 只看該作者
Evaluation of Quantization Techniques for Deep Neural Networks Post-training Quantization and Quantization-aware training. In addition, we also compare the results of different methods on two representative networks in DNNs: Resnet and Mobilenet, and analyse some ablation study. Further more, the remaining questions and future direction are summarized to boost the development of quantization method.
45#
發(fā)表于 2025-3-29 07:35:51 | 只看該作者
Evaluation of the Effectiveness of COVID-19 Prevention and Control Based on Modified SEIR Modeld by examinating Characteristic polynomial, and global stability is proved by constructing Lyapunov Function. In addition, the effect of the epidemic prevention measures are evaluated by numerical simulation. The research shows that the post exposure infection rate and quarantine rate are the most crucial parameters of this disease.
46#
發(fā)表于 2025-3-29 11:52:12 | 只看該作者
47#
發(fā)表于 2025-3-29 18:21:00 | 只看該作者
48#
發(fā)表于 2025-3-29 20:18:12 | 只看該作者
49#
發(fā)表于 2025-3-30 01:54:54 | 只看該作者
50#
發(fā)表于 2025-3-30 07:48:16 | 只看該作者
https://doi.org/10.1007/978-1-4615-0693-5 Post-training Quantization and Quantization-aware training. In addition, we also compare the results of different methods on two representative networks in DNNs: Resnet and Mobilenet, and analyse some ablation study. Further more, the remaining questions and future direction are summarized to boost the development of quantization method.
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