59 citations · 63 across the 3 of their papers we have counts for
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cs.LG2024★ 1 cited
Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs
Zhanke Zhou, Yongqi Zhang, Jiangchao Yao +2
To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query. However, existing m…
cs.LG2023★ 3 cited
A Versatile Graph Learning Approach through LLM-based Agent
Lanning Wei, Huan Zhao, Xiaohan Zheng +2
Designing versatile graph learning approaches is important, considering the diverse graphs and tasks existing in real-world applications. Existing methods have attempted to achieve…
cs.LG2018★ 59 cited
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Bo Han, Gang Niu, Xingrui Yu +4
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end. Although equipped with corrections for noisy labels,…