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An overview on the application of graph neural networks in wireless networks
In recent years, with the rapid enhancement of computing power, deep learning methods
have been widely applied in wireless networks and achieved impressive performance. To …
have been widely applied in wireless networks and achieved impressive performance. To …
Geometric deep learning for drug discovery
Drug discovery is a time-consuming and expensive process. With the development of
Artificial Intelligence (AI) techniques, molecular Geometric Deep Learning (GDL) has …
Artificial Intelligence (AI) techniques, molecular Geometric Deep Learning (GDL) has …
Geometry-enhanced molecular representation learning for property prediction
Effective molecular representation learning is of great importance to facilitate molecular
property prediction. Recent advances for molecular representation learning have shown …
property prediction. Recent advances for molecular representation learning have shown …
Graph neural networks for anomaly detection in industrial Internet of Things
The Industrial Internet of Things (IIoT) plays an important role in digital transformation of
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity
Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …
Recent advances have shown great promise in applying graph neural networks (GNNs) for …
Geomgcl: Geometric graph contrastive learning for molecular property prediction
S Li, J Zhou, T Xu, D Dou, H **ong - … of the AAAI conference on artificial …, 2022 - ojs.aaai.org
Recently many efforts have been devoted to applying graph neural networks (GNNs) to
molecular property prediction which is a fundamental task for computational drug and …
molecular property prediction which is a fundamental task for computational drug and …
Quest: systematically approximating quantum circuits for higher output fidelity
We present QUEST, a procedure to systematically generate approximations for quantum
circuits to reduce their CNOT gate count. Our approach employs circuit partitioning for …
circuits to reduce their CNOT gate count. Our approach employs circuit partitioning for …
Graph convolutional networks in language and vision: A survey
Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …
and have achieved good performance in a range of applications, including social …
Neural Bayes estimators for irregular spatial data using graph neural networks
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast
and likelihood-free manner. Although they are appealing to use with spatial models, where …
and likelihood-free manner. Although they are appealing to use with spatial models, where …
SS-GNN: a simple-structured graph neural network for affinity prediction
S Zhang, Y **, T Liu, Q Wang, Z Zhang, S Zhao… - ACS …, 2023 - ACS Publications
Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due
to the limited computational resources in practical applications and is a crucial basis for drug …
to the limited computational resources in practical applications and is a crucial basis for drug …