Visual Explanations

Visual Explanations is a methodological framework within explainable machine learning and explainable recommendation that employs visual representations to make model decisions interpretable by explicitly highlighting salient input features or decision rationales through graphical or imagery-based outputs.

568

Publications

73.8K

Citations

1.6K

Authors

531

Institutions

Publications per year

2017–2026

460

Authors

1.6K

  1. KM
    Klaus‐Robert Müller

    Technische Universität Berlin, Germany

    9 publications · 6.1K citations

    10

    h-index

  2. WS
    Wojciech Samek

    Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Germany

    10 publications · 6K citations

    10

    h-index

  3. EB
    Enrico Bertini

    New York University, United States

    7 publications · 323 citations

    7

    h-index

  4. ZW
    Ziyan Wu

    Rensselaer Polytechnic Institute, United States

    7 publications · 1.1K citations

    7

    h-index

  5. ER
    Edward R. Tufte

    Princeton University, United States

    6 publications · 1.4K citations

    7

    h-index

1–5 of 1.6K

Venues

  1. arXiv (Cornell University)

    Repository · 96 publications · 6.6K citations

    38

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 13 publications · 445 citations

    13

    h-index

  3. IEEE Access

    10 publications · 348 citations

    11

    h-index

  4. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 7 publications · 949 citations

    8

    h-index

  5. 6

    h-index

1–5