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 …

Biased regularization and fine tuning are two recent meta-learning approaches. They have
been shown to be effective to tackle distributions of tasks, in which the tasks' target vectors …

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 …

An overarching goal in machine learning is to build a generalizable model with few samples.
To this end, overparameterization has been the subject of immense interest to explain the …

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 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 …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Learning an efficient update rule from data that promotes rapid learning of new tasks from
the same distribution remains an open problem in meta-learning. Typically, previous works …

Despite the broad interest in meta-learning, the generalization problem remains one of the
significant challenges in this field. Existing works focus on meta-generalization to unseen …