11 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 11 cited
A Co-training Approach for Noisy Time Series Learning
Weiqi Zhang, Jianfeng Zhang, Jia Li +1
In this work, we focus on robust time series representation learning. Our assumption is that real-world time series is noisy and complementary information from different views of t…
cs.LG2022
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Ziqi Gao, Yifan Niu, Jiashun Cheng +6
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation…
cs.LG2022★ 1 cited
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning
Jiashun Cheng, Man Li, Jia Li +1
Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs. Existing methods can be roughly divided into predictive learning and c…