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Hypergraph Neural Architecture Search

Proceedings of the AAAI Conference on Artificial Intelligence
In recent years, Hypergraph Neural Networks (HGNNs) have achieved considerable success by manually designing architectures, which are capable of extracting effective patterns with high-order interactions from non-Euclidean data. However, such mechanism is extremely inefficient, demanding tremendous human efforts to tune diverse model parameters.
Wei Lin   +3 more
openaire   +1 more source

Disentangled Neural Architecture Search

2022 International Joint Conference on Neural Networks (IJCNN), 2022
Xinyue Zheng   +4 more
openaire   +1 more source

Personalized Neural Architecture Search

2021 International Conference on Data Mining Workshops (ICDMW), 2021
Cedric Kulbach, Steffen Thoma
openaire   +1 more source

A Comprehensive Survey of Neural Architecture Search

ACM Computing Surveys, 2022
Pengzhen Ren   +2 more
exaly  

NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search

IEEE Transactions on Neural Networks and Learning Systems, 2023
Chen Wei, Chuang Niu, Haihong Hu
exaly  

BNAS: Efficient Neural Architecture Search Using Broad Scalable Architecture

IEEE Transactions on Neural Networks and Learning Systems, 2022
Zixiang Ding   +2 more
exaly  

A Survey on Evolutionary Neural Architecture Search

IEEE Transactions on Neural Networks and Learning Systems, 2023
Yuqiao Liu, Yanan Sun, Bing Xue
exaly  

Evolutionary Neural Architecture Search for High-Dimensional Skip-Connection Structures on DenseNet Style Networks

IEEE Transactions on Evolutionary Computation, 2021
Damien O'Neill   +2 more
exaly  

Evolutionary neural architecture search based on evaluation correction and functional units

Knowledge-Based Systems, 2022
Ronghua Shang   +2 more
exaly  

ModuleNet: Knowledge-Inherited Neural Architecture Search

IEEE Transactions on Cybernetics, 2022
Yaran Chen, Ruiyuan Gao, Dongbin Zhao
exaly  

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