Intermediate Representation Selection

Intermediate Representation Selection is the process of choosing a suitable intermediate representation within a compiler framework that balances abstraction fidelity and optimization potential to facilitate efficient code generation and transformation during just-in-time compilation.

70

Publications

7.4K

Citations

268

Authors

111

Institutions

Publications per year

2017–2026

33

Authors

268

  1. AT
    Antonio Torralba

    Massachusetts Institute of Technology, United States

    3 publications · 4.4K citations

    3

    h-index

  2. MC
    Ming‐Wei Chang

    De Montfort University, United Kingdom

    2 publications · 122 citations

    2

    h-index

  3. CV
    Carl Vondrick

    Massachusetts Institute of Technology, United States

    2 publications · 321 citations

    2

    h-index

  4. PH
    Parisa Haghani

    Google (United States), United States

    2 publications · 153 citations

    2

    h-index

  5. RP
    Rohit Prabhavalkar

    Google (United States), United States

    2 publications · 153 citations

    2

    h-index

1–5 of 268

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 270 citations

    7

    h-index

  2. IEEE Transactions on Multimedia

    2 publications · 344 citations

    2

    h-index

  3. 2

    h-index

  4. International Conference on Machine Learning

    Conference · 1 publications · 37 citations

    1

    h-index

  5. The Irish Journal of Psychology

    IJP · 1 publications · 10 citations

    1

    h-index

1–5