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Title
Cited by
Cited by
Year
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks.
X Jiang, P Ji, S Li
IJCAI, 2656-2662, 2019
1002019
Co-embedding of nodes and edges with graph neural networks
X Jiang, R Zhu, P Ji, S Li
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (6), 7075-7086, 2020
892020
The multispecies coalescent model outperforms concatenation across diverse phylogenomic data sets
X Jiang, SV Edwards, L Liu
Systematic biology 69 (4), 795-812, 2020
712020
Cross-domain graph convolutions for adversarial unsupervised domain adaptation
R Zhu, X Jiang, J Lu, S Li
IEEE Transactions on Neural Networks and Learning Systems 34 (8), 3847-3858, 2021
302021
Transferable feature learning on graphs across visual domains
R Zhu, X Jiang, J Lu, S Li
2021 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2021
142021
Mospat: Automl based model selection and parameter tuning for time series anomaly detection
S Chatterjee, R Bopardikar, M Guerard, U Thakore, X Jiang
arXiv preprint arXiv:2205.11755, 2022
92022
Kats
X Jiang, S Srivastava, S Chatterjee, Y Yu, J Handler, P Zhang, ...
Retrieved June 28, 2024, 2022
82022
Self-supervised learning for fast and scalable time series hyper-parameter tuning
P Zhang, X Jiang, G Holt, N Laptev, C Komurlu, P Gao, Y Yu
https://arxiv.org/pdf/2102.05740.pdf, 2021
42021
ODD: Outlier detection and description
S Bhatia, B Hooi, L Akoglu, S Chatterjee, X Jiang, M Gupta
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
22021
Graph convolutional neural networks with edge-node switching
X Jiang
University of Georgia, 2019
2019
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