Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Incremental learning is the ability of systems to acquire knowledge over time, enabling their
adaptation and generalization to novel tasks. It is a critical ability for intelligent, real-world …

Abstract Optical Pooled Screening (OPS) is a powerful tool combining high-content
microscopy with genetic engineering to investigate gene function in disease. The …

Dataset distillation, which condenses large-scale datasets into compact synthetic
representations, has emerged as a critical solution for training modern deep learning …

Unsupervised meta-learning aims to learn feature representations from unsupervised
datasets that can transfer to downstream tasks with limited labeled data. In this paper, we …

Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to
downstream tasks with limited labeled data, sparking considerable interest among …

Self-supervised learning has emerged as a method for utilizing massive unlabeled data for
pre-training models, providing an effective feature extractor for various mobile sensing …

In recent years, self-supervised learning (SSL) has emerged as a promising approach for
extracting valuable representations from unlabeled data. One successful SSL method is …

Meta-learning has been widely used in recent years in areas such as few-shot learning and
reinforcement learning. However, the questions of why and when it is better than other …

Despite the widespread success of deep learning, its intense requirements for vast amounts
of data and extensive training make it impractical for various real-world applications where …