This research explores a novel approach to few-shot learning by introducing metric-agnostic conditional embeddings. The method aims to improve learning from limited data samples by creating flexible representations independent of specific distance metrics.
This paper proposes the "GNN-as-Judge" framework to enhance LLMs' performance in few-shot semi-supervised learning on Text-Attributed Graphs (TAGs) where labeled data is scarce. The method addresses the challenges of generating reliable pseudo-labels and mitigating label noise by incorporating the structural inductive bias of GNNs.
This content explores the ability of Large Language Models (LLMs) to learn health-related tasks with few examples. It discusses how few-shot learning can be effectively applied in the healthcare domain using LLMs.
Mochi is a Graph Foundation Model that improves efficiency and task unification by employing a meta-learning based training framework. It pre-trains on few-shot episodes directly mirroring downstream evaluation, addressing limitations of traditional reconstruction-based pre-training and achieving competitive performance.
This paper introduces SignGAD, a novel framework for few-shot graph anomaly detection that designs task-conditioned detection workflows instead of using fixed anomaly detectors. It addresses challenges of adaptability and weak evidence by selecting suitable graph encodings and detector designs to exploit task-specific anomaly signals.