User-guided Generation

User-Guided Generation is a generative process within interactive systems in which the user actively influences the output formation through iterative inputs, feedback, or constraints, distinguishing it from autonomous or fully automated generation methods by embedding human intent directly into the generative workflow.

87

Publications

4.7K

Citations

282

Authors

98

Institutions

Publications per year

2017–2026

66

Authors

282

  1. SC
    Simon Colton

    Imperial College London, United Kingdom

    3 publications · 61 citations

    3

    h-index

  2. TT
    Tim Teitelbaum

    Cornell University, United States

    3 publications · 537 citations

    3

    h-index

  3. TR
    Thomas Reps

    University of Wisconsin–Madison, United States

    3 publications · 537 citations

    3

    h-index

  4. JG
    Jeremy Gow

    University College London, United Kingdom

    3 publications · 61 citations

    3

    h-index

  5. FW
    Furu Wei

    Microsoft Research Asia (China), China

    3 publications · 172 citations

    3

    h-index

1–5 of 282

Venues

  1. arXiv (Cornell University)

    Repository · 14 publications · 436 citations

    13

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 152 citations

    3

    h-index

  3. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 90 citations

    2

    h-index

  4. Journal of the American Society for Information Science

    JASIS · 2 publications · 88 citations

    2

    h-index

  5. IEEE Transactions on Games

    TG · 1 publications · 17 citations

    1

    h-index

1–5