1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective
Yige Yuan, Bingbing Xu, Huawei Shen +4
Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (Info…
cs.LG2022★ 1 cited
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors
Quan Ding, Shenghua Liu, Bin Zhou +2
Given a multivariate big time series, can we detect anomalies as soon as they occur? Many existing works detect anomalies by learning how much a time series deviates away from what…
cs.LG2022★ 1 cited
Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification
Zhaohui Wang, Qi Cao, Huawei Shen +2
The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test. To achieve high expressive GNNs beyond WL test, we propose a novel graph isomorphism t…