Here, we develop a novel learning rule designed to minimize interferencebetween sequentially learned tasksinrecurrent networks.

The increasing scale of vision transformers (ViT) has made the efficient fine-tuning of these large models for specific needs a significant challenge in various applications.

In this natural setting, it is not common to see the same data sample multiple times, or even twice at times.

Theyaredesigned tohaveagame-theoretic equilibrium where everyone reveals their private information truthfully.