Human pose forecasting is a complex structured-data sequence-modelling task, which has
received increasing attention, also due to numerous potential applications. Research has …

Mixup is an advanced data augmentation method for training neural network based image
classifiers, which interpolates both features and labels of a pair of images to produce …

Graph neural networks (GNNs) have shown state-of-the-art performances in various
applications. However, GNNs often struggle to capture long-range dependencies in graphs …

The prevalence of graph-based data has spurred the rapid development of graph neural
networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets …

Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …

Abstract Temporal Graph Networks (TGNs) are powerful on modeling temporal graph data
based on their increased complexity. Higher complexity carries with it a higher risk of …

Abstract Graph Convolutional Networks (GCNs) have shown promising results in modeling
graph-structured data. However, they have difficulty with processing digraphs because of …

Multivariate time series (MTS) forecasting is an urgent problem for numerous valuable
applications. At present, attention-based methods can relieve recurrent neural networks' …

Graph Convolutional Networks (GCN) which typically follows a neural message passing
framework to model dependencies among skeletal joints has achieved high success in …

Graph neural networks (GNNs) have achieved state-of-the-art performance on graph
classification tasks. Existing work usually feeds graphs to GNNs in random order for training …