107 citations · 115 across the 8 of their papers we have counts for
9 papers
Denoising-Aware Contrastive Learning for Noisy Time Series
Shuang Zhou, Daochen Zha, Xiao Shen +3
Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is l…
TransFlower: An Explainable Transformer-Based Model with Flow-to-Flow Attention for Commuting Flow Prediction
Yan Luo, Zhuoyue Wan, Yuzhong Chen +3
Understanding the link between urban planning and commuting flows is crucial for guiding urban development and policymaking. This research, bridging computer science and urban stud…
Adaptation-Agnostic Meta-Training
Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu +1
Many meta-learning algorithms can be formulated into an interleaved process, in the sense that task-specific predictors are learned during inner-task adaptation and meta-parameters…
Mixed Set Domain Adaptation
Sitong Mao, Keli Zhang, Fu-lai Chung
In the settings of conventional domain adaptation, categories of the source dataset are from the same domain (or domains for multi-source domain adaptation), which is not always tr…
Against Adversarial Learning: Naturally Distinguish Known and Unknown in Open Set Domain Adaptation
Sitong Mao, Xiao Shen, Fu-lai Chung
Open set domain adaptation refers to the scenario that the target domain contains categories that do not exist in the source domain. It is a more common situation in the reality co…
Adversarial Deep Network Embedding for Cross-network Node Classification
Xiao Shen, Quanyu Dai, Fu-lai Chung +2
In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network,…
Variational Metric Scaling for Metric-Based Meta-Learning
Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu +1
Metric-based meta-learning has attracted a lot of attention due to its effectiveness and efficiency in few-shot learning. Recent studies show that metric scaling plays a crucial ro…
Network Together: Node Classification via Cross network Deep Network Embedding
Xiao Shen, Quanyu Dai, Sitong Mao +2
Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network…
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Wenhao Jiang, Cheng Deng, Wei Liu +3
Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically…