The author explores the potential of transformers for online continual learning by leveraging in-context few-shot learning abilities. They propose methods that combine in-context learning and parametric learning to achieve rapid adaptation and sustained progress.
Transformers can be leveraged for online continual learning by combining in-context learning and parametric learning, leading to significant improvements in predictive performance.
Transformers can be leveraged for supervised online continual learning by combining in-context learning and parametric learning, leading to rapid adaptation and sustained progress.
Transformers offer in-context learning abilities for rapid adaptation and sustained progress in supervised online continual learning.