Machine unlearning is a promising solution for efficiently removing specific knowledge by post hoc modifying models.

For (3), we relax our proposed test using techniques from robust statistics and imprecise probabilities.

This setup enables efficient, high-quality visual reconstructions across subjects from one experimental trial.

That is, we propose to formulate continual learning as a sequence modeling problem, allowing advanced sequence models to be utilized for continual learning.