Yihe Deng

Feb 13, 2024 | arxiv.org | Yihe Deng |Weitong Zhang |Quanquan Gu

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Oct 2, 2023 | biorxiv.org | Yihe Deng |Ruochi Zhang |Pan Xu |Jian Ma

AbstractHypergraphs are powerful tools for modeling complex interactions across various domains, including biomedicine. However, learning meaningful node representations from hypergraphs remains a challenge. Existing supervised methods often lack generalizability, thereby limiting their real-world applications.

Oct 2, 2023 | arxiv.org | Yihe Deng |Yuanzhi Li |Quanquan Gu

[Submitted on 2 Oct 2023 ( v1 ), last revised 11 Jul 2024 (this version, v2)] Title:Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP View a PDF of the paper titled Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP, by Zixiang Chen and Yihe Deng and Yuanzhi Li and Quanquan Gu View PDF HTML (experimental) Abstract:Multi-modal learning has become increasingly popular due to its ability to leverage information from different data...