Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

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 …

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 …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Structured state space sequence (S4) models have recently achieved state-of-the-art
performance on long-range sequence modeling tasks. These models also have fast …

Deep reinforcement learning algorithms require large amounts of experience to learn an
individual task. While meta-reinforcement learning (meta-RL) algorithms can enable agents …

Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which
adapts a deep neural network on the edge-labeling graph, for few-shot learning. The …

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