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 …

Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-
learning practice, it can have high computational cost because it updates all model …

Deep neural networks can yield good performance on various tasks but often require large
amounts of data to train them. Meta-learning received considerable attention as one …

Inspired by optimization techniques, we propose a novel meta-learning algorithm with
gradient modulation to encourage fast-adaptation of neural networks in the absence of …

The prevailing literature typically assesses the effectiveness of meta-learning (ML)
approaches on tasks that involve no more than 20 classes. However, we challenge this …

Model-agnostic meta-learning (MAML) is one of the most popular and widely adopted meta-
learning algorithms, achieving remarkable success in various learning problems. Yet, with …

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

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 …

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 …

Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-
learning, where a meta-model is learned with the ability of fast adapting to new tasks …