Continuous Sequence Learning
Continuous Sequence Learning is a machine learning framework within sequence learning that enables models to incrementally acquire and adapt to new sequential patterns over time without forgetting previously learned knowledge, distinguishing it from batch or episodic sequence learning approaches.
18
Publications
1.3K
Citations
59
Authors
28
Institutions
Publications per year
2017–2026
10
Parents
18
1–5 of 18
59
2
h-index
2
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1–5 of 59
28
New York University
New York, United States
2 h-index
16 publications
1.6K citations
Yale University
New Haven, United States
2 h-index
2 publications
194 citations
Max Planck Institute for Empirical Aesthetics
Frankfurt am Main, Germany
2 h-index
2 publications
194 citations
University of Chicago
Chicago, United States
1 h-index
3 publications
66 citations

Italian Institute of Technology
Genoa, Italy
1 h-index
1 publications
48 citations
Southern States University
San Diego, United States
1 h-index
2 publications
40 citations
University of Nevada, Las Vegas
Las Vegas, United States
1 h-index
1 publications
20 citations
UCLouvain
Louvain-la-Neuve, Belgium
1 h-index
3 publications
144 citations
1–8 of 28
1 publications · 11 citations
1
h-index
1 publications · 48 citations
1
h-index
1 publications · 123 citations
1
h-index
1 publications · 413 citations
1
h-index
1 publications · 20 citations
1
h-index
1–5