most citedA Co-training Approach for Noisy Time Series Learning

11 citations · 12 across the 3 of their papers we have counts for

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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…