Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.

Graph property prediction tasks are important and numerous.

The"other category" included hallucinations, repetitiveoutputs, and symbol mapping errors.

Machine learning models based on temporal point processes arethe state ofthe artinawide variety ofapplications involving discrete events incontinuous time.

For example, the déjà vu method shows that for certain representation learning models and training images, it is sometimes possible to correctly predict the foreground label given only the representation of the background - better than through dataset-level correlations.