Machine Learning Attribution

Machine Learning Attribution is a scholarly contribution within computational linguistics and artificial intelligence that, as a formal framework or methodological construct, specifically enables the systematic identification and evaluation of source origins for proverbial expressions in digital text corpora through data-driven modeling and algorithmic inference.

51

Publications

6.2K

Citations

170

Authors

54

Institutions

Publications per year

2017–2026

41

Authors

170

  1. MJ
    Michael J. Collins

    Queensland University of Technology, Australia

    4 publications · 116 citations

    4

    h-index

  2. DD
    Dipanjan Das

    Google (United States), United States

    4 publications · 116 citations

    4

    h-index

  3. SP
    Slav Petrov

    Google (United States), United States

    4 publications · 116 citations

    4

    h-index

  4. GS
    Gaurav Singh Tomar

    Google (United States), United States

    3 publications · 90 citations

    3

    h-index

  5. DR
    David Reitter

    Pennsylvania State University, United States

    3 publications · 90 citations

    3

    h-index

1–5 of 170

Venues

  1. arXiv (Cornell University)

    Repository · 15 publications · 3.5K citations

    14

    h-index

  2. Neural Information Processing Systems

    Conference · 2 publications · 93 citations

    2

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 102 citations

    2

    h-index

  4. Literary and Linguistic Computing

    LLC · 2 publications · 214 citations

    2

    h-index

  5. Computational Linguistics

    2 publications · 56 citations

    2

    h-index

1–5