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Unsupervised Image Super-Resolution with an Indirect Supervised Path.

S. Chen, Z. Han, E. Dai, X. Jia, Z. Liu, X. Liu, X. Zou, C. Xu, J. Liu, и Q. Tian. CVPR Workshops, стр. 1924-1933. Computer Vision Foundation / IEEE, (2020)

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Huangdong Dai

Jing Dai

Yangguang Dai

Min Dai

Vodang Dai

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Learning Fair Graph Neural Networks With Limited and Private Sensitive Attribute Information.E. Dai, и S. Wang. IEEE Trans. Knowl. Data Eng., 35 (7): 7103-7117 (июля 2023)Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network.Y. Hu, X. Cheng, S. Wang, J. Chen, T. Zhao, и E. Dai. CoRR, (2021)Learning Graph Filters for Spectral GNNs via Newton Interpolation.J. Xu, E. Dai, D. Luo, X. Zhang, и S. Wang. CoRR, (2023)Labeled Data Generation with Inexact Supervision.E. Dai, K. Shu, Y. Sun, и S. Wang. KDD, стр. 218-226. ACM, (2021)A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy.E. Dai, L. Cui, Z. Wang, X. Tang, Y. Wang, M. Cheng, B. Yin, и S. Wang. KDD, стр. 368-379. ACM, (2023)Label-Wise Graph Convolutional Network for Heterophilic Graphs.E. Dai, S. Zhou, Z. Guo, и S. Wang. LoG, том 198 из Proceedings of Machine Learning Research, стр. 26. PMLR, (2022)Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels.E. Dai, W. Jin, H. Liu, и S. Wang. WSDM, стр. 181-191. ACM, (2022)Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features.T. Zhao, E. Dai, K. Shu, и S. Wang. WSDM, стр. 1433-1442. ACM, (2022)Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series.E. Dai, и J. Chen. ICLR, OpenReview.net, (2022)Unsupervised Image Super-Resolution with an Indirect Supervised Path.S. Chen, Z. Han, E. Dai, X. Jia, Z. Liu, X. Liu, X. Zou, C. Xu, J. Liu, и Q. Tian. CVPR Workshops, стр. 1924-1933. Computer Vision Foundation / IEEE, (2020)

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