15 citations · 19 across the 7 of their papers we have counts for
5 papers · 1 filter
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Yinjun Wu, Jingchao Ni, Wei Cheng +7
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging…
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu +4
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along edges of the given graph.…
T-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Denghui Zhang, Yanchi Liu, Wei Cheng +5
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional tur…
Learning Robust Representations with Graph Denoising Policy Network
Lu Wang, Wenchao Yu, Wei Wang +5
Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing p…
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Chuxu Zhang, Dongjin Song, Yuncong Chen +7
Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in mult…