Distribution shifts in multi-dimensional data, caused by one or more "corrupted" dimensions, are

Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data.

Studying protein mutations within amino acid sequences holds tremendous significance in life sciences.

Diffusion generative models, as an emerging research focus, have been applied to graph generation tasks.

They do not study how the population loss decreases during the first stage.