AEGIS is an edge resampling framework that enhances link prediction in edge-sparse bipartite graphs, showing benefits from semantic augmentation on text-rich data.
The relationship between precision-recall and roc curves
2 Pith papers cite this work. Polarity classification is still indexing.
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Uncertainty-aware fine-tuning with a decision-theory-based loss produces better-calibrated uncertainty estimates than standard training on free-form QA tasks.
citing papers explorer
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AEGIS: Authentic Edge Growth In Sparsity for Link Prediction in Edge-Sparse Bipartite Knowledge Graphs
AEGIS is an edge resampling framework that enhances link prediction in edge-sparse bipartite graphs, showing benefits from semantic augmentation on text-rich data.
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Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
Uncertainty-aware fine-tuning with a decision-theory-based loss produces better-calibrated uncertainty estimates than standard training on free-form QA tasks.