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
Parents
54
Statistically Significant Detection of Linguistic Change
Vivek Kulkarni, Rami Al‐Rfou, Bryan Perozzi et al. · 2015 · 395 citations
1–5 of 54
198
2
h-index
2
h-index
2
h-index
1
h-index
1
h-index
1–5 of 198
89

Vanderbilt University
Nashville, United States
2 h-index
4 publications
98 citations

Chitkara University
Chandigarh, India
2 h-index
1 publications
16 citations

Zhejiang University
Hangzhou, China
2 h-index
3 publications
136 citations

Maulana Azad National Institute of Technology
Bhopal, India
1 h-index
3 publications
144 citations
Istanbul Health and Technology University
Istanbul, Türkiye
1 h-index
1 publications
33 citations

Icahn School of Medicine at Mount Sinai
New York, United States
1 h-index
1 publications
13 citations

University of Liverpool
Liverpool, United Kingdom
1 h-index
1 publications
19 citations
Louisiana State University
Baton Rouge, United States
1 h-index
1 publications
20 citations
1–8 of 89
Repository · 5 publications · 85 citations
5
h-index
4 publications · 125 citations
4
h-index
2 publications · 39 citations
2
h-index
2 publications · 114 citations
2
h-index
1 publications · 20 citations
1
h-index
1–5