1 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting
Linghang Kong, Elynn Chen, Yuzhou Chen +1
Multi-dimensional time series data, such as matrix and tensor-variate time series, are increasingly prevalent in fields such as economics, finance, and climate science. Traditional…
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks
Yujia Wu, Bo Yang, Yang Zhao +3
Graph Neural Networks (GNNs) have become the de facto standard for analyzing graph-structured data, leveraging message-passing techniques to capture both structural and node featur…
Conditional Prediction ROC Bands for Graph Classification
Yujia Wu, Bo Yang, Elynn Chen +2
Graph classification in medical imaging and drug discovery requires accuracy and robust uncertainty quantification. To address this need, we introduce Conditional Prediction ROC (C…
Tensor-Fused Multi-View Graph Contrastive Learning
Yujia Wu, Junyi Mo, Elynn Chen +1
Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from unlabeled graph-structured…
Tensor-view Topological Graph Neural Network
Tao Wen, Elynn Chen, Yuzhou Chen
Graph classification is an important learning task for graph-structured data. Graph neural networks (GNNs) have recently gained growing attention in graph learning and have shown s…