Graph Neural Network Aware of Explicit Feature Interaction (EFIA-GNN)Termite life cycle optimizer (TLCO)Network of wireless sensors (WSN),Heterogeneous Wireless Sensor NetworksAn array of sensing nodes that are
这篇文章是华为诺亚实验室在GCN的基础上,加入邻居节点交互感知完成的一个推荐系统方向的论文,提出了模型NIA-GCN,被发表在SIGIR 2020上,文中关于邻居交互感知的部分,与BGNN模型比较像(可以参考本人CSDN《论文阅读》专栏 - 『论文《Bilinear Graph Neural Network with Neighbor Interactions》阅读』一文)。 论文地址:...
Graph Neural Networks for Intelligent Transportation Systems: A Survey 2023, IEEE Transactions on Intelligent Transportation Systems Intention-Aware Vehicle Trajectory Prediction Based on Spatial-Temporal Dynamic Attention Network for Internet of Vehicles ...
Kosaraju V, Sadeghian A, Martín-Martín R et al (2019) Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. In: Proceedings of Annual Conference on Neural Information Processing Systems. NeurIPS, Vancouver, pp 1–10 Mohamed A, Qian K, Elhoseiny M et...
When the attention matrix is obtained, it is formulated into a propagation matrix for graph neural networks. Finally, a Time-extrapolator Convolutional Neural Network (TXP-CNN) is used in the temporal dimension of the aggregated features to predict the future trajectories of the pedestrians. ...
DialogueGCN: 一种用于会话情感识别的图形卷积神经网络 DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation 语言环境建模的重要性,序列模型现有缺点是还是遗忘问题,从图的方面,改进了序列模型的缺点 0. Abstract 对话情感识别(Emotion recogn... ...
The interaction context is modeled as a graph structure, and series of base host features are first extracted from the graph, and then a graph neural network (GNN) is utilized to generate enhanced host features, where latent interaction patterns are mined through the high-order structural ...
Learning Convolutional Neural Networks for Graphs. In Proceedings of the 33rd International Conference on Machine Learning (ICML), New York, NY, USA, 19–24 June 2016. [Google Scholar] Zhao, L.; Peng, X.; Tian, Y.; Kapadia, M.; Metaxas, D.N. Semantic Graph Convolutional Networks for...
In this paper, we propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents through space and time. To encourage longsighted robot behaviors, we infer the intentions of dynamic agents by predicting their future trajectories for several ...
To address such issues, this study proposes a novel approach, namely knowledge-aware interaction networks for domain-adaptive E2E-ABSA. Specifically, we construct domain subgraphs using external knowledge. Subsequently, we retrieve “correlative words” connecting two domains in the graph and identify ...