Graph Neural Networks (GNNs) conduct message passing which aggregates lo...
Out-of-distribution (OOD) graph generalization are critical for many
rea...
Autoencoders are widely used in outlier detection due to their superiori...
Diffusion models are a class of deep generative models that have shown
i...
Unsupervised/self-supervised graph representation learning is critical f...
Unsupervised/self-supervised representation learning in time series is
c...
Graph embedding methods including traditional shallow models and deep Gr...
Unsupervised/self-supervised time series representation learning is a
ch...
Few-shot Time Series Classification (few-shot TSC) is a challenging prob...
We extend this idea further to explicitly model the distribution-level
r...
Long-term prediction of multivariate time series is still an important b...