Papers by Masashi Sugiyama
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification (2024.emnlp-main)
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| Challenge: | Recent advances in fine-tuning Vision-Language Models have seen the success of prompt tuning and adapter tuning. |
| Approach: | They propose a method to fine-tune CLIP without introducing any overhead of extra parameters. |
| Outcome: | The proposed method improves CLIP by 7.27% average harmonic mean accuracy. |
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning (2021.eacl-main)
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| Challenge: | Current methods for document set expansion for large collections are based on word-frequency or bag-of-words document similarity metrics. |
| Approach: | They propose to extend the IR approach by treating the problem as an instance of positive-unlabeled (PU) learning . they propose solutions for each challenge and empirically validate them with ablation tests . |
| Outcome: | The proposed method improves on a PubMed abstract retrieval task . it is compared with existing methods and empirically validated with ablation tests . |
Learning Only from Relevant Keywords and Unlabeled Documents (D19-1)
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| Challenge: | Existing methods for document classification are limited due to labeling and privacy concerns. |
| Approach: | They propose a super-vised text classification framework that provides keywords as a hint for classifying a document to a target class. |
| Outcome: | The proposed framework is simple to implement and has flexible choices of models, e.g., linear models or neural networks. |