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Seonghyun Ban

2 accepted papers

2024

Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning

NeurIPS 2024poster

Semi-supervised learning (SSL) seeks to utilize unlabeled data to overcome the limited amount of labeled data and improve model performance. However, many SSL methods typically struggle in real-world scenarios, particularly when there is a large number of irrelevant instances in the unlabeled data t…

Cited by 0SourcePDFScholar
2024

Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL

ACL 2024long

With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning regards selecting appropriate keywords included into the input, thereby adapting to the downstream task without adjusting o…