JAZZ
发表于 2025-3-21 17:13:51
书目名称Making, Makers, Makerspaces影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0621806<br><br> <br><br>书目名称Making, Makers, Makerspaces读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0621806<br><br> <br><br>
ingestion
发表于 2025-3-21 23:17:12
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maroon
发表于 2025-3-22 02:51:21
Janette Hughes,Stephanie Thompson,Laura Morrisont summarization. We observe that self-training and cross-training a pre-trained model with . selected data shows competitive performance to the pre-trained model. Furthermore, a small amount of . selected data is sufficient for domain adaptation against fine-tuning on the entire training dataset wit
使痛苦
发表于 2025-3-22 08:04:41
Janette Hughes,Jennifer Laffier,Jennifer A. Robbf optimization (minimal and maximal) in the bipartite matching process, which provides more flexibility. Our evaluation shows, we each scored 0.966, 0.990, and 0.996 .@1 rates on the . dataset in Chinese, Japanese, and French to English alignment tasks. We outperformed the state-of-the-art method in
notice
发表于 2025-3-22 11:43:01
Janette Hughes,Stephanie Thompsonies from the ontology. We evaluate the effect of using symbolic knowledge from ontologies with graph neural networks. Experimental results on two public biomedical datasets, BioRel and ADE, show that our method outperforms all the baselines (approximately by 3%).
alleviate
发表于 2025-3-22 15:51:43
Janette Hughes,Laura Morrisonof neighbor nodes (for edge removal) and probable future neighbor nodes (for edge insertion) on the core number of a given node. Accordingly, we define Removal Strength and Insertion Strength measures to capture the resilience of an individual node upon removing and inserting an edge, respectively.
破布
发表于 2025-3-22 20:35:19
Janette Hughes,Laura Morrisonning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning...Part V:. Robustness; Time Series; Transfer and Multitask Learning...Part VI:. Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Comput
intrude
发表于 2025-3-22 23:11:20
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SOW
发表于 2025-3-23 05:06:25
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manifestation
发表于 2025-3-23 07:42:51
Janette Hughes,Margie Lamces and, as a result, simultaneously extracts latent higher-order spatio-temporal dependencies. We provide theoretical foundations behind the proposed hyper-simplex-graph representation learning and validate our new Hodge-style Hyper-simplex-graph Neural Networks (H.-Nets) on 7 real world spatio-tem