Meta-learning methods have shown an impressive ability to train models that rapidly learn
new tasks. However, these methods only aim to perform well in expectation over tasks …

The ability to learn new concepts with small amounts of data is a critical aspect of
intelligence that has proven challenging for deep learning methods. Meta-learning has …

Despite the empirical success of deep meta-learning, theoretical understanding of
overparameterized meta-learning is still limited. This paper studies the generalization of a …

Meta-learning algorithms aim to learn two components: a model that predicts targets for a
task, and a base learner that updates that model when given examples from a new task. This …

The goal of optimization-based meta-learning is to find a single initialization shared across a
distribution of tasks to speed up the process of learning new tasks. Conditional meta …

In this paper, we study the generalization properties of Model-Agnostic Meta-Learning
(MAML) algorithms for supervised learning problems. We focus on the setting in which we …

Meta-learning aims to perform fast adaptation on a new task through learning a “prior” from
multiple existing tasks. A common practice in meta-learning is to perform a train-validation …

Meta-learning aims to use the knowledge from previous tasks to facilitate the learning of
novel tasks. Many meta-learning models elaborately design various task-shared inductive …

Abstract Model-Agnostic Meta-Learning (MAML) and its variants have achieved success in
meta-learning tasks on many datasets and settings. Nonetheless, we have just started to …

Meta-learning has proven to be a powerful paradigm for transferring the knowledge from
previous tasks to facilitate the learning of a novel task. Current dominant algorithms train a …