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Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this

Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently.

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Date Created
  • 2019
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  • Text
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    • Partial requirement for: M.S., Arizona State University, 2019
      Note type
      thesis
    • Includes bibliographical references
      Note type
      bibliography
    • Field of study: Computer science

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    by Jaya Vijetha Gattupalli

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