Continual Learning
Continual Learning is a machine learning paradigm in which a model (or autonomous agent) learns from a non-stationary stream of data or sequence of tasks by updating its parameters incrementally over time while retaining and integrating previously acquired knowledge without having simultaneous access to all past data.
1.4K
Publications
106.1K
Citations
4.1K
Authors
970
Institutions
Publications per year
2017–2026
1K
1.4K
Learning to Forget: Continual Prediction with LSTM
Felix A. Gers, Jürgen Schmidhuber, Fred Cummins · Neural Computation · 2000 · 5.4K citations
Learning to forget: continual prediction with LSTM
Felix A. Gers · 1999 · 2.4K citations
Increased rates of convergence through learning rate adaptation
Robert A. Jacobs · Neural Networks · 1988 · 1.8K citations
1–5 of 1.4K
4.1K
20
h-index
17
h-index
14
h-index
13
h-index
12
h-index
1–5 of 4.1K
970
Tsinghua University
Beijing, China
25 h-index
106 publications
7.8K citations
Google (United States)
Mountain View, United States
25 h-index
93 publications
10.6K citations

Peking University
Beijing, China
22 h-index
85 publications
3.5K citations

Chinese Academy of Sciences
Beijing, China
21 h-index
98 publications
4.1K citations
Carnegie Mellon University
Pittsburgh, United States
21 h-index
46 publications
5.5K citations
Nanyang Technological University
Singapore, Singapore
20 h-index
56 publications
6.1K citations
University of Illinois Chicago
Chicago, United States
19 h-index
36 publications
2.3K citations

University of Bologna
Bologna, Italy
17 h-index
59 publications
4.2K citations
1–8 of 970
Repository · 290 publications · 14.9K citations
58
h-index
Conference · 35 publications · 3.1K citations
26
h-index
Conference · 24 publications · 3K citations
24
h-index
26 publications · 1.4K citations
22
h-index
TPAMI · 25 publications · 3.8K citations
22
h-index
1–5