惊惶
发表于 2025-3-26 23:56:35
Vikas Gupta,Satish Kumar,Disha Kamboj,Chandra Nath Mishra,Charan Singh,Gyanendra Singh,Gyanendra Prato-end training. As a state-of-the-art end-to-end framework, the Knowledge-aware Graph Neural Networks with Label Smoothness Regularization (KGNN-LS) model can extend GNNs architecture to knowledge graphs to simultaneously capture semantic relations between entities as well as personalized user pref
博识
发表于 2025-3-27 03:23:45
Rama Shankar,Vikas Dwivedi,Gulab Chand AryaPrevious work has applied active learning to classification with partially observed data. However, for large and sparse data, the number of feedbacks to be queried is huge and many of them are invalid. In this paper, we develop an active classification framework that can address these challenges by
grovel
发表于 2025-3-27 08:48:47
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GOAD
发表于 2025-3-27 12:10:12
Abhay K. Pandey,Ajit Kumar Savani,Pooja Singhtructures, and it is an NP-hard problem. And the successful application of Particle Swarm Optimization (PSO) technique in this field also reflects its extraordinary optimization ability. Therefore, based on Social Learning Particle Swarm Optimization (SLPSO), this paper proposes an XSMT construction
是比赛
发表于 2025-3-27 13:43:26
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不能妥协
发表于 2025-3-27 18:59:16
Ranjana Gautam,Rajesh Kumar Meena,Gulab Khan Rohela,Naveen Kumar Singh,Pawan Shuklae., record re-identification through some released columns, in the datasets is a fatal problem that prevents these tasks. Therefore, evaluating the sensitivity for different attributes is a prerequisite for dataset desensitization and anonymization, after which datasets can be published and shared i
BILK
发表于 2025-3-27 23:37:53
Rachapudi Venkata Sreeharsha,Shalini Mudalkar,Divya K. Unnikrishnan,S. Venkata Mohan,Attipalli R. ReIt is necessary to understand the face-to-face (e.g. Device-to-Device, D2D) social network structure and to predict content propagation precisely, which can be conducted by learning the low-dimensional embedding of the network nodes, called Network Representation Learning (NRL). However, most existi
Irremediable
发表于 2025-3-28 03:22:09
Israr Ahmed,Pawan Shukla,Ranjana Gautamtween the attributes of different entities in KG, which could be utilized to improve the performance, remain largely unexploited. In this paper, we propose an end-to-end .nowledge .raph attention network enhanced .equential .ecommendation (KGSR) framework to capture the context-dependency of sequenc
FLAIL
发表于 2025-3-28 09:05:05
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是剥皮
发表于 2025-3-28 13:23:52
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