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Found 4 papers, 1 papers with code
Navigating Conflicting Views: Harnessing Trust for Learning
Resolving conflicts is essential to make the decisions of multi-view classification more reliable.
Enhancing Near OOD Detection in Prompt Learning: Maximum Gains, Minimal Costs
In this study, we investigate the near OOD detection capabilities of prompt learning models and observe that commonly used OOD scores have limited performance in near OOD detection.
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Uncertainty estimation is essential to make neural networks trustworthy in real-world applications.
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples.
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