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cs.LG2024
Class-aware and Augmentation-free Contrastive Learning from Label Proportion
Jialiang Wang, Ning Zhang, Shimin Di +2
Learning from Label Proportion (LLP) is a weakly supervised learning scenario in which training data is organized into predefined bags of instances, disclosing only the class label…
cs.LG2024
Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models
Jialiang Wang, Hanmo Liu, Shimin Di +4
High-level automation is increasingly critical in AI, driven by rapid advances in large language models (LLMs) and AI agents. However, LLMs, despite their general reasoning power,…
cs.LG2024
Cross-domain-aware Worker Selection with Training for Crowdsourced Annotation
Yushi Sun, Jiachuan Wang, Peng Cheng +3
Annotation through crowdsourcing draws incremental attention, which relies on an effective selection scheme given a pool of workers. Existing methods propose to select workers base…