Results 71 to 80 of about 149,463 (272)

Learnable Graph Convolutional Attention Networks

CoRR, 2022
Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features. Recent works have shown the strengths and weaknesses of the resulting GNN architectures, respectively, GCNs and GATs.
Adrián Javaloy, Pablo Sánchez-Martín, Amit Levi, Isabel Valera   +3 more
openaire   +3 more sources

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

Dual-channel deep graph convolutional neural networks

Frontiers in Artificial Intelligence
The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhuoran Li, Zhuoran Li, Zhuoran Li, Zhuoran Li, Gege Li, Gege Li, Gege Li, Gege Li, Haixing Zhao, Haixing Zhao, Haixing Zhao, Haixing Zhao   +15 more
doaj   +1 more source

Source Localization of Network Information Propagation via Invertible Graph Diffusion [PDF]

Jisuanji kexue yu tansuo
With the development of society, security issues in various types of networks have become increasingly prominent, especially network propagation issues.
ZHAI Wenshuo, ZHAO Xiang, CHEN Dong
doaj   +1 more source

Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification

Frontiers in Neurorobotics, 2021
The graph neural network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered.
Shuhao Shi, Kai Qiao, Shuai Yang, Linyuan Wang, Jian Chen, Bin Yan   +5 more
doaj   +1 more source

Vision‐Augmented Wearable Interfaces: Bioinspired Approaches for Realistic AI‐Human‐Machine Interaction

Advanced Materials Technologies, EarlyView.
This review presents recent progress in vision‐augmented wearable interfaces that combine artificial vision, soft wearable sensors, and exoskeletal robots. Inspired by biological visual systems, these technologies enable multimodal perception and intelligent human–machine interaction.
Jihun Lee, Gwuidong Ryu, Kisoo Kim, Feng Xu, Won Gu Lee   +4 more
wiley   +1 more source

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source

Human Action Recognition Algorithm Based on Adaptive Shifted Graph Convolutional NeuralNetwork with 3D Skeleton Similarity [PDF]

Jisuanji kexue
Graph convolutional neural network(GCN) has achieved good results in the field of human action recognition based on 3D skeleton.However,in most of the existing GCN methods,the construction of the behavior diagram is based on the manual setting of the ...
YAN Wenjie, YIN Yiying
doaj   +1 more source

Exploiting Weak Ties in Incomplete Network Datasets Using Simplified Graph Convolutional Neural Networks

Machine Learning and Knowledge Extraction, 2020
This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption ...
Neda H. Bidoki, Alexander V. Mantzaris, Gita Sukthankar   +2 more
doaj   +1 more source

DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting

, 2020
Traffic speed forecasting is one of the core problems in Intelligent Transportation Systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through ...
Lee, Kyungeun, Rhee, Wonjong
core