Parallel Inference

Parallel Inference is a scalable machine learning method that enables the simultaneous execution of multiple inference tasks across distributed computing resources to reduce overall latency and improve throughput.

169

Publications

8.2K

Citations

588

Authors

201

Institutions

Publications per year

2017–2026

67

Authors

588

  1. JE
    Joseph E. Gonzalez

    University of California, Berkeley, United States

    5 publications · 982 citations

    5

    h-index

  2. YX
    Yinglong Xia

    University of Illinois Urbana-Champaign, United States

    5 publications · 98 citations

    5

    h-index

  3. VK
    Viktor K. Prasanna

    University of Southern California, United States

    5 publications · 98 citations

    5

    h-index

  4. CG
    Carlos Guestrin

    Carnegie Mellon University, United States

    4 publications · 873 citations

    4

    h-index

  5. YL
    Yucheng Low

    Carnegie Mellon University, United States

    4 publications · 873 citations

    4

    h-index

1–5 of 588

Venues

  1. arXiv (Cornell University)

    Repository · 24 publications · 1.8K citations

    18

    h-index

  2. Computer

    4 publications · 301 citations

    4

    h-index

  3. Future Generation Computer Systems

    FGCS · 4 publications · 80 citations

    4

    h-index

  4. New Generation Computing

    3 publications · 67 citations

    3

    h-index

  5. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 168 citations

    3

    h-index

1–5