Concepedia

Training-time Pruning

Training-Time Pruning is a model optimization technique that belongs to the class of network pruning methods, distinguished by its execution of weight or neuron elimination concurrently with the training process to enhance efficiency and reduce computational overhead.

54

Publications

2.7K

Citations

193

Authors

72

Institutions

Publications per year

2017–2026

42

Parents

Publications

54

Model Pruning Enables Efficient Federated Learning on Edge Devices

Yuang Jiang, Shiqiang Wang, Víctor Valls et al. · IEEE Transactions on Neural Networks and Learning Systems · 2022 · 522 citations

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Picking Winning Tickets Before Training by Preserving Gradient Flow

Chaoqi Wang, Guodong Zhang, Roger Grosse · arXiv (Cornell University) · 2020 · 148 citations · Full text

, , +15

Stabilizing the Lottery Ticket Hypothesis

Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy et al. · arXiv (Cornell University) · 2019 · 146 citations · Full text

, , +15

The Lottery Ticket Hypothesis: Training Pruned Neural Networks.

Jonathan Frankle, Michael Carbin · 2018 · 111 citations

, , +10

(LP) 2 , an Adaptive Algorithm for Information Extraction from Web-related Texts

Fabio Ciravegna · 2001 · 110 citations

, , +14

1–5 of 54

Authors

193

  1. JF
    Jonathan Frankle

    Massachusetts Institute of Technology, United States

    5 publications · 483 citations

    5

    h-index

  2. MC
    Michael Carbin

    Massachusetts Institute of Technology, United States

    5 publications · 483 citations

    5

    h-index

  3. DM
    Daniel M. Roy

    University of Toronto, Canada

    4 publications · 372 citations

    4

    h-index

  4. GK
    Gintare Karolina Dziugaite

    University of Toronto, Canada

    4 publications · 372 citations

    4

    h-index

  5. VV
    Víctor Valls

    Trinity College Dublin, Ireland

    2 publications · 577 citations

    2

    h-index

1–5 of 193

Institutions

72

  1. University of Toronto

    Toronto, Canada

    4 h-index

    6 publications

    480 citations

  2. Massachusetts Institute of Technology

    Cambridge, United States

    3 h-index

    4 publications

    206 citations

  3. Nanyang Technological University

    Singapore, Singapore

    3 h-index

    9 publications

    282 citations

  4. City University of Hong Kong

    Hong Kong, Hong Kong

    2 h-index

    2 publications

    69 citations

  5. Beihang University

    Beijing, China

    2 h-index

    6 publications

    206 citations

  6. Meta (Israel)

    Tel Aviv, Israel

    2 h-index

    4 publications

    70 citations

  7. Xidian University

    Xi'an, China

    2 h-index

    4 publications

    143 citations

  8. Hong Kong Polytechnic University

    Hong Kong, Hong Kong

    1 h-index

    4 publications

    116 citations

1–8 of 72

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 671 citations

    9

    h-index

  2. Neurocomputing

    5 publications · 195 citations

    5

    h-index

  3. IEEE Access

    2 publications · 42 citations

    2

    h-index

  4. Neural Computing and Applications

    2 publications · 33 citations

    2

    h-index

  5. 1

    h-index

1–5