Context-sensitive Transfer Functions

Context-Sensitive Transfer Functions is a transfer function design method that adapts its transformation behavior based on the surrounding or preceding computational, structural, or semantic context, thereby distinguishing itself from context-insensitive transfer functions by dynamically modulating output according to environmental or conditional input features.

9

Publications

655

Citations

38

Authors

15

Institutions

Publications per year

2017–2026

2

Authors

38

  1. BC
    Ben-Chung Cheng

    University of Illinois Urbana-Champaign, United States

    2 publications · 209 citations

    2

    h-index

  2. WH
    Wen‐mei Hwu

    University of Illinois Urbana-Champaign, United States

    2 publications · 209 citations

    2

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  3. MD
    Mark Dunn

    Honda (Germany), Germany

    1 publications · 39 citations

    1

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  4. VD
    Vasco Diogo

    Vrije Universiteit Amsterdam, Netherlands

    1 publications · 14 citations

    1

    h-index

  5. SK
    Stephan Kirstein

    Honda (Germany), Germany

    1 publications · 39 citations

    1

    h-index

1–5 of 38

Venues

  1. International Journal of Neural Systems

    1 publications · 39 citations

    1

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  2. ACM SIGPLAN Notices

    1 publications · 43 citations

    1

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  3. 1

    h-index

  4. Journal of Vision

    1 publications · 30 citations

    1

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  5. Learning and Motivation

    1 publications · 38 citations

    1

    h-index

1–5