Model Chaining

Model Chaining is a model integration technique that sequentially composes multiple models by feeding the output of one model as input to the next, thereby enabling cascaded processing and dependency propagation across stages.

7

Publications

195

Citations

22

Authors

11

Institutions

Publications per year

2017–2026

1

Variants

Model Cascading

Authors

22

  1. GT
    George T. Heineman

    Columbia University, United States

    2 publications · 53 citations

    2

    h-index

  2. GE
    Gail E. Kaiser

    Columbia University, United States

    2 publications · 53 citations

    2

    h-index

  3. IB
    I. Ben-Shaul

    Columbia University, United States

    2 publications · 53 citations

    2

    h-index

  4. NS
    Naser S. Barghouti

    Columbia University, United States

    2 publications · 53 citations

    2

    h-index

  5. RC
    Ricardo Cerri

    Universidade Federal de São Carlos, Brazil

    1 publications · 69 citations

    1

    h-index

1–5 of 22

Venues

  1. International Conference on Control, Automation and Systems

    Conference · 1 publications · 15 citations

    1

    h-index

  2. IEEE Expert

    1 publications · 42 citations

    1

    h-index

  3. arXiv (Cornell University)

    Repository · 1 publications · 13 citations

1–3