4 papers
How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark
Yingxuan Cheng, Tong Li, Yayong Li +5
Soil organic carbon (SOC) is an indicator of agricultural soil condition and carbon storage, but numerical outputs from large language models (LLMs) require validation before use i…
Inductive Graph Few-shot Class Incremental Learning
Yayong Li, Peyman Moghadam, Can Peng +2
Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well known in contrast to Graph Few-Shot Class Incremental Learning (GFSCIL), which involves lea…
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning
Xinyi Gao, Yayong Li, Tong Chen +3
With the increasing computation of training graph neural networks (GNNs) on large-scale graphs, graph condensation (GC) has emerged as a promising solution to synthesize a compact,…
Graph Condensation for Open-World Graph Learning
Xinyi Gao, Tong Chen, Wentao Zhang +3
The burgeoning volume of graph data presents significant computational challenges in training graph neural networks (GNNs), critically impeding their efficiency in various applicat…