Proposes a look-ahead variant of MAML for online continual learning that modulates per-parameter learning rates. By meta-learning the learning rate for each parameter, La-MAML can dynamically control plasticity and stability on a per-parameter basis.
2019
Adapts Model-Agnostic Meta-Learning (MAML) for continual learning by meta-learning representations that are robust to catastrophic forgetting. The learned representations enable fast adaptation to new tasks with minimal interference to previously learned ones.
2020
Uses a neuromodulatory network to gate the activations of a prediction network during meta-learning. This biologically-inspired gating mechanism learns to protect important representations while allowing plasticity for new learning, achieving strong continual few-shot performance.
2019
Combines experience replay with meta-learning by formulating the replay buffer optimization as a meta-learning problem. MER treats each training example as a separate task and uses Reptile-style meta-updates to find parameters that work well across all examples in the buffer.