Transformers for Supervised Online Continual Learning: Leveraging In-Context Few-Shot Learning Abilities
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 for Supervised Online Continual Learning: Exploring In-Context Learning Abilities
Transformers can be leveraged for online continual learning by combining in-context learning and parametric learning, leading to significant improvements in predictive performance.
Transformers for Supervised Online Continual Learning: Leveraging In-Context Learning for Fast Adaptation and Long-Term Improvement
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 for Supervised Online Continual Learning: Leveraging In-Context Learning Abilities for Fast Adaptation and Sustained Progress
Transformers offer in-context learning abilities for rapid adaptation and sustained progress in supervised online continual learning.
