Invariant Representation Learning

Invariant Representation Learning is a representation learning paradigm that derives data embeddings which preserve task-relevant information while remaining unchanged under specified transformations or variations in the input space.

283

Publications

19.9K

Citations

786

Authors

281

Institutions

Publications per year

2017–2026

170

Authors

786

  1. TP
    Tomaso Poggio

    Massachusetts Institute of Technology, United States

    12 publications · 335 citations

    11

    h-index

  2. FA
    Fabio Anselmi

    McGovern Institute for Brain Research, United States

    9 publications · 312 citations

    9

    h-index

  3. LR
    Lorenzo Rosasco

    University of Genoa, Italy

    9 publications · 306 citations

    9

    h-index

  4. JB
    Joan Bruna

    New York University, United States

    5 publications · 212 citations

    5

    h-index

  5. MW
    Max Welling

    University of Amsterdam, Netherlands

    5 publications · 560 citations

    5

    h-index

1–5 of 786

Venues

  1. arXiv (Cornell University)

    Repository · 83 publications · 5K citations

    33

    h-index

  2. Neural Networks

    6 publications · 161 citations

    6

    h-index

  3. Pattern Recognition

    5 publications · 253 citations

    5

    h-index

  4. Neural Information Processing Systems

    Conference · 5 publications · 220 citations

    5

    h-index

  5. IEEE Transactions on Image Processing

    5 publications · 299 citations

    5

    h-index

1–5