Traffic state prediction (Graph Neural Network-based Traffic trajectory prediction) 缺点:需要真实的数据集。 SAST-GNN: a self-attention based spatio-temporal graph neural network for traffic prediction Multi-modal trajectory prediction for autonomous driving with semantic map and dynamic graph attention net...
Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications. However, their node memory favors smaller batch sizes to capture more dependencies in graph events and needs to be maintained ...
Dynamic reconfigurable computingGraph neural networkData storagePrefetcherVertex reorderingGraph neural networks (GNNs) have achieved great success in processing non-Euclidean geometric spatial data structures. However, the irregular memory access of aggregation and the power-law distribution of the real-world...
In fact, before the rise of deep learning, the industry has already begun to explore the technology of Graph Embedding[1]. The early graph embedding algorithms were mostly based on heuristic matrix decomposition and probabilistic graph models; later, more "shallow" neural network models represented ...
In the last decade, a plethora of graph neural network (GNN) subcategories have been proposed to address the unique challenges of learning on graph-structured data. These subcategories include the graph convolutional networks (GCNs), which leverage convolutional operations on graph data to capture ...
Graph learning, graph neural network, distributed training, workflow, computational pattern, communication pattern, optimization technique, software framework. 图形学习、图形神经网络、分布式训练、工作流、计算模式、通信模式、优化技术、软件框架。 INTRODUCTION ...
决策优化旅行商问题GNN《Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP》《Attention solves your tsp》https://github.com/machine-reasoning-ufrgs/TSP-GNNhttps://github.com/wouterkool/attention-tsp 决策优化规划器调度GNN《Adaptive Planner Scheduling with Graph Neural Netw...
GNNs created in AllegroGraph enhance neural network methods by processing the graph data through rounds of message passing, as such, the nodes know more about their own features as well as neighbor nodes. This creates an even more accurate representation of the entire graph network. AllegroGraph ...
Runtime Performance Prediction for Deep Learning Models with Graph Neural Network Yanjie Gao1, Xianyu Gu1, 3∗, Hongyu Zhang2, Haoxiang Lin1†, Mao Yang1 1Microsoft Research, Beijing, China Email: {yanjga, haoxlin, maoyang}@microsoft.com 2Chongqing University, Chongqing, China Email: ...
图的重分区是图分区的一种扩展,它处理动态图(dynamic graphs),即顶点和边的集合随着时间的推移而修改的图。 假设一个图是被分区的,其结构的动态变化会使其块不平衡,使其边割或顶点割更大。 图7说明了图结构随时间的演变,随后进行了重新分区。通常,每次修改整个图时,都可以从头开始对其进行分区,从而获得重新分区...