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 …

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 …

An effective approach in meta-learning is to utilize multiple “train tasks” to learn a good
initialization for model parameters that can help solve unseen “test tasks” with very few …

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 …

We address the problem of meta-learning which learns a prior over hypothesis from a
sample of meta-training tasks for fast adaptation on meta-testing tasks. A particularly simple …

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 …

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 …

As a popular meta-learning approach, the model-agnostic meta-learning (MAML) algorithm
has been widely used due to its simplicity and effectiveness. However, the convergence of …

With the growing attention on learning-to-learn new tasks using only a few examples, meta-
learning has been widely used in numerous problems such as few-shot classification …