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Proposes an analytic federated learning framework that requires only one or two communication rounds for multi-label medical image classification under task heterogeneity, outperforming existing methods on ChestXray14 by up to 18.44 BACC and 13.24 AUC points.
The paper proposes Label Influence Propagation (LIP), a model that analyzes and propagates label influences in graph neural networks for multi-label node classification, consistently outperforming state-of-the-art methods.
This paper proposes a zero-shot multi-label topic classification framework enhanced with per-article knowledge graphs, comparing four base variants and their graph-augmented counterparts across fifteen LLMs and eight datasets. The study finds that keyword-enhanced classification performs best, and graph augmentation improves small models but degrades performance in larger ones.
Introduces TADDLE, a tool-augmented agent for detecting deficient LLM-generated peer reviews, along with an expert-annotated benchmark of 1,800 reviews on 50 ICLR 2025 papers. The system decomposes detection into four specialized analysis tools and uses two-stage semi-supervised learning for binary and multi-label classification.
This paper proposes a retrieval-based approach for multi-label legal annotation that uses frozen embedding models to retrieve labels via k-nearest neighbors, achieving competitive accuracy, high data efficiency, and eliminating label hallucination by design.