Meta-learning automatically infers an inductive bias by observing data from a number of
related tasks. The inductive bias is encoded by hyperparameters that determine aspects of …

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from
data corresponding to multiple related tasks with the goal of improving the sample efficiency …

Meta-learning optimizes an inductive bias—typically in the form of the hyperparameters of a
base-learning algorithm—by observing data from a finite number of related tasks. This paper …

Meta-learning aims at optimizing the hyperparameters of a model class or training algorithm
from the observation of data from a number of related tasks. Following the setting of Baxter …

Meta learning automatically infers an inductive bias, that includes the hyperparameter of the
base-learning algorithm, by observing data from a finite number of related tasks. This paper …

Meta learning automatically infers an inductive bias, that includes the hyperparameter of the
baselearning algorithm, by observing data from a finite number of related tasks. This paper …

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 …

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 …

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 …

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 …