Cache Inference Models

Cache Inference Models is a computational framework that integrates caching mechanisms with probabilistic inference to optimize the reuse of previously computed results in machine learning pipelines by dynamically predicting and retrieving high-value intermediate representations.

5

Publications

307

Citations

23

Authors

9

Institutions

Publications per year

2017–2026

2

Authors

23

  1. NH
    Nguyen H. Tran

    Kyung Hee University, South Korea

    1 publications · 173 citations

    1

    h-index

  2. KC
    Kuan-Hsun Chen

    TU Dortmund University, Germany

    1 publications · 19 citations

    1

    h-index

  3. CS
    Chiahui Su

    University of Twente, Netherlands

    1 publications · 19 citations

    1

    h-index

  4. JL
    Jenq‐Kuen Lee

    National Tsing Hua University, Taiwan

    1 publications · 19 citations

    1

    h-index

  5. AB
    Azer Bestavros

    Boston University, United States

    1 publications · 26 citations

    1

    h-index

1–5 of 23

Venues

  1. ACM Transactions on Embedded Computing Systems

    TECS · 1 publications · 19 citations

    1

    h-index

  2. 1

    h-index

  3. Knowledge-Based Systems

    1 publications · 47 citations

    1

    h-index

  4. arXiv (Cornell University)

    Repository · 1 publications · 10 citations

1–4