BANAL 发表于 2025-3-23 10:30:26
Centralized Reasoning Translation and Its Computing Complexity for Heterogeneous Semantic Mappings. The current research focuses on the homogeneous bridge rules that only contain atomic elements. In this paper, the research is extended to the D3L reasoning problem with heterogeneous bridge rules that contain composite elements in subset ends. The regularity of the distributed knowledge base is dSenescent 发表于 2025-3-23 16:08:42
http://reply.papertrans.cn/55/5441/544044/544044_12.png背景 发表于 2025-3-23 20:09:42
http://reply.papertrans.cn/55/5441/544044/544044_13.png托人看管 发表于 2025-3-23 23:03:07
http://reply.papertrans.cn/55/5441/544044/544044_14.png高调 发表于 2025-3-24 05:51:03
http://reply.papertrans.cn/55/5441/544044/544044_15.png可商量 发表于 2025-3-24 09:07:20
http://reply.papertrans.cn/55/5441/544044/544044_16.png芭蕾舞女演员 发表于 2025-3-24 12:35:28
http://reply.papertrans.cn/55/5441/544044/544044_17.png漂泊 发表于 2025-3-24 18:09:27
http://reply.papertrans.cn/55/5441/544044/544044_18.png圆桶 发表于 2025-3-24 20:16:07
A Network Embedding and Clustering Algorithm for Expert Recommendation Serviceeatures of various tasks based on graphs, including classification, clustering, link prediction and visualization. Currently, network embedding algorithms have evolved from considering structures only to considering structures and contents both. However, how to effectively integrate the high-order placrimal-gland 发表于 2025-3-25 00:14:45
Mixing-RNN: A Recommendation Algorithm Based on Recurrent Neural Networktering algorithms face challenges to provide accurate recommendation when users’ interest and context suddenly changed. In this paper, we present a new Recurrent Neural Network-based model, namely Mixing-RNN that is able to capture time and context changes for item recommendation. In particular, Mix