Temporal knowledge graph embedding learningSpatial-temporal data miningTemporal knowledge graph completion, which aims to predict missing links in temporal knowledge graph (TKG), is an important research task due to the incompleteness of TKG. Recently, TKG embedding......
11 Learning from Highly Sparse Spatio-temporal Data 12 UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction D&B Track 13 LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation 14 TorchSpatial: A Location Encoding Framework and Benchmark for Spa...
本文总结了CIKM 2024有关时空数据(spatial-temporal data)的相关论文,主要包含交通预测,插补,事故预测,气象预测,轨迹相似度计算,物流配送以及时空图神经网络在金融,供应链,能源等领域应用的相关内容,如有疏漏,欢迎大家补充。 Full Research 1 Prompt-Based Spatio-Temporal Graph Transfer Learning 链接:arxiv.org/abs/...
2024 IJCAI(International Joint Conference on Artificial Intelligence, 国际人工智能联合会议)在2024年8月3日-9日正在韩国济州岛举行。 本文总结了IJCAI2024有关时空数据(Spatial-temporal)的相关论文,如有疏漏,欢迎大家补充。 时空数据Topic:时空(交通)预测,气象预测,轨迹表示学习,轨迹恢复,信控优化,POI等 1. Spatial...
NeurIPS 2024于2024年12月10号-12月15号在加拿大温哥华举行(Vancouver, Canada),录取率25.8%本文总结了NeurIPS 2024有关时空数据(spatial-temporal data)的相关论文,主要包含气象预测,时空插补,轨迹生成,交通模拟,信控优化,异常检测以及LLM在时空数据的应用等内容,如有疏漏,欢迎大家补充。时空数据Topic:气象预测,时空插补...
摘要: Temporal knowledge graph completion, which aims to predict missing links in temporal knowledge graph (TKG), is an important research task due to the incompleteness of TKG. Recently, TKG embedding...关键词: Temporal knowledge graph completion Temporal knowledge graph embedding learning Spatial-...
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition回到顶部 摘要动态人体骨架模型带有进行动作识别的重要信息,传统的方法通常使用手工特征或者遍历规则对骨架进行建模,从而限制了表达能力并且很难去泛化。作者提出了一个新颖的动态骨架模型ST-GCN,它可以从数据中自动地学习空间和时间的pattern...
However, this task faces challenges such intraindividual action differences and long-term temporal dependencies. To address these challenges, we propose an innovative model called spatial-temporal graph neural ordinary differential equations (STG-NODE). First, in the data preprocessing stage, the dynamic...
graph笔记 Jumping Knowledge Networks 论文:Representation Learning on Graphs with Jumping Knowledge Networks 1、传统GCN在不同阶数上会有不同表现 随着阶数增加,所获取的信息更加全面,也就可以解释为啥大部分论文的GCN的层数只有2层,而之后提出的randomwalk的cotraining也依旧会导致这个问题当ran... ...
SpatialTemporalGraphConvolutionalNetworksforS。。。Spa tia l Tempo r a l Gr a ph Co nvo lutio na l N etw o rk s fo r Sk eleto n-Ba sed Ac tio n l Tem po l Gra R ec o gnitio n 摘要 动态⼈体⾻架模型带有进⾏动作识别的重要信息，传统的⽅法通常使⽤⼿⼯特征或者遍历...