Titlebook: Advances in Knowledge Discovery and Data Mining; 28th Pacific-Asia Co De-Nian Yang,Xing Xie,Jerry Chun-Wei Lin Conference proceedings 2024

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书目名称Advances in Knowledge Discovery and Data Mining影响因子(影响力)




书目名称Advances in Knowledge Discovery and Data Mining影响因子(影响力)学科排名




书目名称Advances in Knowledge Discovery and Data Mining网络公开度




书目名称Advances in Knowledge Discovery and Data Mining网络公开度学科排名




书目名称Advances in Knowledge Discovery and Data Mining被引频次




书目名称Advances in Knowledge Discovery and Data Mining被引频次学科排名




书目名称Advances in Knowledge Discovery and Data Mining年度引用




书目名称Advances in Knowledge Discovery and Data Mining年度引用学科排名




书目名称Advances in Knowledge Discovery and Data Mining读者反馈




书目名称Advances in Knowledge Discovery and Data Mining读者反馈学科排名

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978-981-97-2265-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/148641.jpg

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P. M. Parizel,H. Tanghe,P. A. M. Hofman These schemes typically require setting parameters to appropriately model the problem at hand. We study the problem of parameter selection for applications that rely on simulations, where standard methods like grid search are computationally prohibitive. Our solution supports engineers in setting p

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Magnetic Resonance Imaging of the Brain,erent modalities and obtain a unified representation in the same space. WCAN exploits an adversarial training method to add perturbations to text features to enhance model robustness. Specifically, we devise a weighted cross-modal aggregation (WCA) module that measures the distance between text, ima

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James F. M. Meaney,John Sheehan,Mathias Boose, especially from biased knowledge introduction. In this work, we propose KiProL, a knowledge-injected prompt learning framework to improve language generation and training efficiency. KiProL tackles ineffective learning and utilization of knowledge, reduces the biased knowledge introduction, as we

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James F. M. Meaney,John Sheehanis challenge diverges from traditional VQA by requiring models to identify a bounding box in response to an image-question pair, aligning with Visual Grounding tasks. Existing VG approaches, when applied to GVQA, often necessitate external data or larger models for satisfactory results, leading to h