Abstract: Recently, self-supervised learning has shown great potential in Graph Neural Networks (GNNs) through contrastive learning, which aims to learn discriminative features for each node without ...
Abstract: This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. For multi-robots to efficiently perform ...
This is the code for SGIR, a semi-supervised framework for Graph Imbalanced Regression. Data imbalance is easily found in annotated data when the observations of certain continuous label values are ...
Objective: Suicidality is highly prevalent with vast public health implications. The association between schizophrenia spectrum disorders (SSDs) and suicidal ideation and related behaviors (SIBs) is ...
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