Saliency Visualization

Saliency Visualization is a computational method that belongs to the class of interpretability techniques in artificial intelligence, specifically distinguished by its ability to highlight the most influential input features or regions in a model’s decision-making process to enhance transparency and pedagogical explainability in tutoring systems.

11

Publications

433

Citations

39

Authors

18

Institutions

Publications per year

2017–2026

8

Authors

39

  1. YZ
    Yang Zhang

    University of Sheffield, United Kingdom

    2 publications · 108 citations

    2

    h-index

  2. AP
    Ankit Patel

    Rice University, United States

    2 publications · 108 citations

    2

    h-index

  3. AW
    Alexander Wong

    University of Waterloo, Canada

    2 publications · 82 citations

    2

    h-index

  4. WN
    Weili Nie

    Rice University, United States

    2 publications · 108 citations

    2

    h-index

  5. GW
    Graham W. Taylor

    University of Guelph, Canada

    2 publications · 82 citations

    2

    h-index

1–5 of 39

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 119 citations

    3

    h-index

  2. Electronics

    1 publications · 10 citations

    1

    h-index

  3. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 105 citations

    1

    h-index

  4. Proceedings of the ACM on Human-Computer Interaction

    PACMHCI · 1 publications · 36 citations

    1

    h-index

  5. Computers & Education

    1 publications · 11 citations

    1

    h-index

1–5