Deep Learning-based Change-point Detection

Deep Learning-Based Change-Point Detection is a methodological framework within change-point detection that leverages deep neural networks to automatically learn complex temporal patterns and identify distributional shifts in sequential data, distinguishing itself from traditional statistical or shallow learning approaches through its capacity for hierarchical feature extraction and modeling of non-linear dynamics.

54

Publications

2.4K

Citations

198

Authors

89

Institutions

Publications per year

2017–2026

49

Authors

198

  1. GS
    Gurwinder Singh

    Chitkara University, India

    1 publications · 16 citations

    2

    h-index

  2. SS
    Sartajvir Singh

    Chitkara University, India

    1 publications · 16 citations

    2

    h-index

  3. VS
    Vishakha Sood

    Chitkara University, India

    1 publications · 16 citations

    2

    h-index

  4. WC
    Wei-Cheng Chang

    Carnegie Mellon University, United States

    1 publications · 15 citations

    1

    h-index

  5. QZ
    Qiaosheng Zhang

    New York University, United States

    1 publications · 17 citations

    1

    h-index

1–5 of 198

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 85 citations

    5

    h-index

  2. 4

    h-index

  3. Sensors

    2 publications · 39 citations

    2

    h-index

  4. Computers and Electronics in Agriculture

    2 publications · 114 citations

    2

    h-index

  5. Information

    1 publications · 20 citations

    1

    h-index

1–5