Shared Representation Learning

Shared Representation Learning is a multi-task learning approach that belongs to the category of methods which construct a common latent space across related tasks, distinguished by its mechanism of enforcing parameter sharing or feature alignment to improve generalization through inductive transfer.

217

Publications

15K

Citations

757

Authors

280

Institutions

Publications per year

2017–2026

156

Authors

757

  1. XQ
    Xipeng Qiu

    Fudan University, China

    5 publications · 1.9K citations

    5

    h-index

  2. JY
    Jieping Ye

    Arizona State University, United States

    5 publications · 650 citations

    5

    h-index

  3. RM
    Risto Miikkulainen

    The University of Texas at Austin, United States

    4 publications · 74 citations

    4

    h-index

  4. PL
    Pengfei Liu

    Fudan University, China

    4 publications · 1.8K citations

    4

    h-index

  5. XH
    Xuanjing Huang

    Fudan University, China

    4 publications · 1.8K citations

    4

    h-index

1–5 of 757

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 3.2K citations

    22

    h-index

  2. 7

    h-index

  3. Pattern Recognition Letters

    6 publications · 242 citations

    6

    h-index

  4. IEEE Transactions on Cybernetics

    5 publications · 355 citations

    5

    h-index

  5. Pattern Recognition

    4 publications · 274 citations

    4

    h-index

1–5