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Titlebook: Web and Big Data; 7th International Jo Xiangyu Song,Ruyi Feng,Geyong Min Conference proceedings 2024 The Editor(s) (if applicable) and The

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樓主: 爆發(fā)
41#
發(fā)表于 2025-3-28 18:05:28 | 只看該作者
,An Investigation of?the?Effectiveness of?Template Protection Methods on?Protecting Privacy During I detection model. Eventually, during the spoof detection phase, protected templates are used as input, rather than the original iris images. Experiments conducted on CASIA-Syn and CASIA-Interval datasets demonstrate that the application of iris template protection techniques to the spoof detection m
42#
發(fā)表于 2025-3-28 22:12:46 | 只看該作者
,Stock Volatility Prediction Based on?Transformer Model Using Mixed-Frequency Data,s part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86.
43#
發(fā)表于 2025-3-29 01:46:45 | 只看該作者
,An Investigation of?the?Effectiveness of?Template Protection Methods on?Protecting Privacy During I detection model. Eventually, during the spoof detection phase, protected templates are used as input, rather than the original iris images. Experiments conducted on CASIA-Syn and CASIA-Interval datasets demonstrate that the application of iris template protection techniques to the spoof detection m
44#
發(fā)表于 2025-3-29 04:34:00 | 只看該作者
45#
發(fā)表于 2025-3-29 11:10:58 | 只看該作者
,Stock Volatility Prediction Based on?Transformer Model Using Mixed-Frequency Data,s part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86.
46#
發(fā)表于 2025-3-29 14:13:18 | 只看該作者
47#
發(fā)表于 2025-3-29 18:10:54 | 只看該作者
48#
發(fā)表于 2025-3-29 22:04:43 | 只看該作者
49#
發(fā)表于 2025-3-30 03:13:57 | 只看該作者
,A Multi-teacher Knowledge Distillation Framework for?Distantly Supervised Relation Extraction with?rature regulation (FTR) to adjust the temperature assigned to each training instance, so as to dynamically capture local relations between instances. Furthermore, we introduce information entropy of hidden layers to gain stable temperature calculations. Finally, we propose multi-view knowledge disti
50#
發(fā)表于 2025-3-30 04:16:42 | 只看該作者
,PAEE: Parameter-Efficient and?Data-Effective Image Captioning Model with?Knowledge Prompter and?Croed models and similar approaches, while reducing the number of trainable parameters. We design two new datasets to explore the data utilization ability of PAEE and discover that it can effectively use new data and achieve domain transfer without any training or fine-tuning. Additionally, we introduc
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