In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

In this paper, we address the few-shot classification task from a new perspective of optimal
matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to …

Learning with limited data is a key challenge for visual recognition. Many few-shot learning
methods address this challenge by learning an instance embedding function from seen …

Abstract Machine learning research has advanced in multiple aspects, including model
structures and learning methods. The effort to automate such research, known as AutoML …

The reviews had two major concerns: lack of a benchmarking on a complex dataset, and
unclear writing. To address these two major issues we: 1-Rewrote experiments section with …

A fundamental assumption of most machine learning algorithms is that the training and test
data are drawn from the same underlying distribution. However, this assumption is violated …

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 …

We study the convergence of a class of gradient-based Model-Agnostic Meta-Learning
(MAML) methods and characterize their overall complexity as well as their best achievable …