Task Similarity Learning

Task Similarity Learning is a meta-learning framework that quantifies the relatedness between learning tasks through learned or computed similarity metrics, enabling knowledge transfer and model adaptation by distinguishing tasks based on shared structural or functional characteristics within a task distribution.

51

Publications

1.8K

Citations

141

Authors

68

Institutions

Publications per year

2017–2026

35

Authors

141

  1. SJ
    Sung Ju Hwang

    Korea Advanced Institute of Science and Technology, South Korea

    3 publications · 87 citations

    3

    h-index

  2. EY
    Eunho Yang

    Korea Advanced Institute of Science and Technology, South Korea

    3 publications · 87 citations

    3

    h-index

  3. JY
    Jaehong Yoon

    Korea Advanced Institute of Science and Technology, South Korea

    3 publications · 87 citations

    3

    h-index

  4. GL
    Giwoong Lee

    Korea Advanced Institute of Science and Technology, South Korea

    3 publications · 87 citations

    3

    h-index

  5. WJ
    Wonyong Jeong

    Korea Advanced Institute of Science and Technology, South Korea

    3 publications · 87 citations

    3

    h-index

1–5 of 141

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 196 citations

    7

    h-index

  2. Neural Information Processing Systems

    Conference · 4 publications · 143 citations

    4

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 184 citations

    3

    h-index

  4. 2

    h-index

  5. Psychological Review

    1 publications · 63 citations

    1

    h-index

1–5