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Titlebook: Advanced Data Mining and Applications; 19th International C Xiaochun Yang,Heru Suhartanto,Ningning Cui Conference proceedings 2023 The Edit

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樓主: implicate
31#
發(fā)表于 2025-3-26 23:32:44 | 只看該作者
32#
發(fā)表于 2025-3-27 04:59:15 | 只看該作者
Bernard Shaw‘s Marriages and Misalliancesct extensive experiments on 6 benchmark NER datasets, 3 of which are nested NER tasks. The experiments show that: (a) Our proposed convolutional bypass method can significantly improve the overall performances of the multi-exit BERT biaffine NER model. (b) our proposed early exiting mechanisms can e
33#
發(fā)表于 2025-3-27 07:26:09 | 只看該作者
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發(fā)表于 2025-3-27 09:39:31 | 只看該作者
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發(fā)表于 2025-3-27 17:27:06 | 只看該作者
36#
發(fā)表于 2025-3-27 19:34:16 | 只看該作者
https://doi.org/10.1007/978-3-031-46661-8artificial intelligence; computational linguistics; computer networks; computer systems; computer vision
37#
發(fā)表于 2025-3-27 22:15:26 | 只看該作者
978-3-031-46660-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
38#
發(fā)表于 2025-3-28 04:48:00 | 只看該作者
Development of the Comic Sublime fragmentary and incomplete with censored intervals or missing data, making it hard for downstream prediction and decision-making tasks. In this work, we propose a fresh extension on the definition of the temporal point process, which conventionally characterizes chronological prediction based on hi
39#
發(fā)表于 2025-3-28 06:43:12 | 只看該作者
Relaxation of the Comic Sublimeaces. Deep learning methods have recently achieved promising performance thanks to their powerful representation learning capacity. However, existing deep learning-based classifiers rely solely on temporal information while disregarding clues from the frequency perspective. In this regard, we propos
40#
發(fā)表于 2025-3-28 13:40:52 | 只看該作者
Bernard Shaw and the Comic SublimeCurrent Transformer-based models routinely use positional embedding for their position-sensitive modules while no efforts are paid to evaluating its effectiveness in specific problems. In this paper, we explore the impact of positional embedding on the vanilla Transformer and six Transformer-based v
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