近年来,transformer网络在NLP领域占据主导地位[43,10,26,52,50]。Transformer模型完全抛弃了递归性,而将注意力集中在跨时间step的关注上。该架构允许长期依赖建模和大规模并行训练。transformer结构也已成功应用于其他领域,如股票预测[30]、机器人决策[12]等。STAR将Transformer的思想应用于图序列。我们在一个具有挑战性...
在时间维度上,对每个行人单独考虑,应用temporal Transformer抽取时许相关性; 即使是时许上的Transformer,也提供了比RNN更好的表现; 在空间维度上,引入TGConv--Transformer-based message passing graph convolution mechanism。相较于传统的图卷积抽取行人之间的交互关系,采用TGConv在高人群密度、复杂交互关系的情形下能...
TransformerGraph neural networksUnderstanding crowd motion dynamics is critical to real-world applications,\ne.g., surveillance systems and autonomous driving. This is challenging because\nit requires effectively modeling the socially aware crowd spatial interaction\nand complex temporal dependencies. We ...
Transformer-Based Spatiotemporal Graph Diffusion Convolution Network for Traffic Flow ForecastingGRAPH neural networksTRAFFIC estimationTRAFFIC flow... C Wang - 《Electronics》 被引量: 0发表: 2024年 Traffic Flow Forecasting Based on Transformer with Diffusion Graph Attention Network Traffic Flow Forecasting...
论文精读|2024[KDD]ImputeFormer: 用于广义时空补全的低秩诱导的Transformer ImputeFormer 21. Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks 链接:https://arxiv.org/abs/2406.08287 ACM链接:https://dl.acm.org/doi/abs/10.1145/3637528.3671912 ...
This research proposes an optimizing wind power prediction model through attention mechanism and spatiotemporal graph neural networks. Initially, the spectral clustering and a self-adjacency matrix to construct the graph nodes and edges. Subsequently, the proposed ASTGNN combined from graph convolutional ...
STCGCN: a spatio-temporal complete graph convolutional network for remaining useful life prediction of power transformer Mengda Xing, Weilong Ding, Tianpu Zhang, Han Li International Journal of Web Information Systems ISSN: 1744-0084 Article publication date: 6 July 2023 Permissions Issue publica...
Traffic prediction is a vital aspect of Intelligent Transportation Systems with widespread applications. The main challenge is accurately modeling the complex spatial and temporal relationships in traffic data. Spatial鈥搕emporal Graph Neural Networks (GNNs) have emerged as one of the most promising meth...
Spatial-Temporal Transformer Networks for Traffic Flow Forecastingarxiv.org/abs/2001.02908 It presents a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies with self-attention mechanism to capture real-time traffic conditions as well as...
Gao J, Zhang T, Xu C (2019) Graph convolutional tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4649–4659 Gao N, Xue H, Shao W, Zhao S, Qin KK, Prabowo A, Rahaman MS, Salim FD (2020) Generative adversarial networks for spatio-tempor...