Automated Feature Learning

Automated Feature Learning is a computational methodology within the field of machine learning that systematically discovers and extracts relevant patterns or representations from raw data without human intervention, distinguishing itself by its reliance on algorithmic inference rather than manual feature engineering.

286

Publications

17.2K

Citations

1K

Authors

467

Institutions

Publications per year

2017–2026

199

Authors

1K

  1. HS
    Horst Samulowitz

    IBM (United States), United States

    5 publications · 597 citations

    5

    h-index

  2. MZ
    Mengjie Zhang

    Victoria University of Wellington, New Zealand

    5 publications · 397 citations

    5

    h-index

  3. UK
    Udayan Khurana

    IBM (United States), United States

    4 publications · 529 citations

    4

    h-index

  4. BX
    Bing Xue

    Victoria University of Wellington, New Zealand

    4 publications · 294 citations

    4

    h-index

  5. AJ
    Anuchit Jitpattanakul

    King Mongkut's University of Technology North Bangkok, Thailand

    3 publications · 386 citations

    3

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 15 publications · 972 citations

    13

    h-index

  2. IEEE Access

    6 publications · 274 citations

    6

    h-index

  3. Scientific Reports

    5 publications · 275 citations

    5

    h-index

  4. Sensors

    4 publications · 534 citations

    5

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 974 citations

    3

    h-index

1–5