@inproceedings{wang-etal-2023-wizmap,
    title = "{W}iz{M}ap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings",
    author = "Wang, Zijie J.  and
      Hohman, Fred  and
      Chau, Duen Horng",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.50",
    doi = "10.18653/v1/2023.acl-demo.50",
    pages = "516--523",
    abstract = "Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for interpreting trained models, building new models, and analyzing new datasets. However, interpreting and using embeddings can be challenging due to their opaqueness, high dimensionality, and the large size of modern datasets. To tackle these challenges, we present WizMap, an interactive visualization tool to help researchers and practitioners easily explore large embeddings. With a novel multi-resolution embedding summarization method and a familiar map-like interaction design, WizMap enables users to navigate and interpret embedding spaces with ease. Leveraging modern web technologies such as WebGL and Web Workers, WizMap scales to millions of embedding points directly in users{'} web browsers and computational notebooks without the need for dedicated backend servers. WizMap is open-source and available at the following public demo link: \url{https://poloclub.github.io/wizmap}.",
}

Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for interpreting trained models, building new models, and analyzing new datasets. However, interpreting and using embeddings can be challenging due to their opaqueness, high dimensionality, and the large size of modern datasets. To tackle these challenges, we present WizMap, an interactive visualization tool to help researchers and practitioners easily explore large embeddings. With a novel multi-resolution embedding summarization method and a familiar map-like interaction design, WizMap enables users to navigate and interpret embedding spaces with ease. Leveraging modern web technologies such as WebGL and Web Workers, WizMap scales to millions of embedding points directly in users{'} web browsers and computational notebooks without the need for dedicated backend servers. WizMap is open-source and available at the following public demo link: \url{https://poloclub.github.io/wizmap}.
Wang, Zijie J. Hohman, Fred Chau, Duen Horng
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings