Papers by Fred Hohman

1 papers
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings (2023.acl-demo)

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Challenge: Modern machine learning models learn latent embedding representations that capture the domain semantics of training data.
Approach: They propose an interactive visualization tool to help users explore large embeddings by using a multi-resolution summarization method and a familiar map-like interface.
Outcome: The proposed visualization tool scales to millions of embedding points directly in users’ web browsers and computational notebooks without the need for dedicated backend servers.

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