Few-shot meta-learning presents a challenge for gradient descent optimization due to the
limited number of training samples per task. To address this issue, we propose an episodic …

A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on
prior experience. Gradient (or optimization) based meta-learning has recently emerged as …

In this paper, we hypothesize that gradient-based meta-learning (GBML) implicitly
suppresses the Hessian along the optimization trajectory in the inner loop. Based on this …

Meta-learning (ML) has emerged as a promising direction in learning models under
constrained resource settings like few-shot learning. The popular approaches for ML either …

Optimization-based meta-learning algorithms are a powerful class of methods for learning-to-
learn applications such as few-shot learning. They tackle the limited availability of training …

Few-shot learning is an emerging yet challenging problem in which the goal is to achieve
generalization from only few examples. Meta-learning tackles few-shot learning via the …

Meta-learning approaches have been proposed to tackle the few-shot learning problem.
Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable …

Abstract We introduce Meta-AdaM, a meta-learned adaptive optimizer with momentum,
designed for few-shot learning tasks that pose significant challenges to deep learning …

Meta-learning (ML) has emerged as a promising learning method under resource
constraints such as few-shot learning. ML approaches typically propose a methodology to …

Gradient-based meta-learning has earned a widespread popularity in few-shot learning, but
remains broadly impractical for tasks with long horizons (many gradient steps), due to …