Model-agnostic meta-learning (MAML) is arguably one of the most popular meta-learning
algorithms nowadays. Nevertheless, its performance on few-shot classification is far behind …

Distributed machine learning on edges is widely used in intelligent transportation, smart
home, industrial manufacturing, and underground pipe network monitoring to achieve low …

We introduce an efficient optimization-based meta-learning technique for large-scale neural
field training by realizing significant memory savings through automated online context point …

The idea of using a separately trained target model (or teacher) to improve the performance
of the student model has been increasingly popular in various machine learning domains …

Meta learning approaches to few-shot classification are computationally efficient at test time,
requiring just a few optimization steps or single forward pass to learn a new task, but they …

Over the past decade, deep neural networks have demonstrated significant success using
the training scheme that involves mini-batch stochastic gradient descent on extensive …

The gradient-based meta learning and its approximation algorithms have been widely used
in the few-shot scenarios. In practice, it is common for the trained meta-model to employ …

Multilingual models jointly pretrained on multiple languages have achieved remarkable
performance on various multilingual downstream tasks. Moreover, models finetuned on a …

Meta-learning (ML) utilizes extracted meta-knowledge from data to enable models to
perform well on unseen data that they have not encountered before. Typically, this meta …

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can
quickly adapt to new environments and tasks. In this work, we study the MRL problem under …