Masked Language Modeling
Masked Language Modeling is a self-supervised learning method in natural language processing where the model is trained to predict randomly masked tokens in a sequence based on their contextual representations, distinguishing it from other language modeling approaches by its bidirectional context utilization and explicit reconstruction objective.
411
Publications
34.9K
Citations
1.6K
Authors
345
Institutions
Publications per year
2017–2026
411
411
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
Hao Tan, Mohit Bansal · 2019 · 2.3K citations · Full text
ProteinBERT: a universal deep-learning model of protein sequence and function
Nadav Brandes, Dan Ofer, Yam Peleg et al. · Bioinformatics · 2022 · 975 citations · Full text
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Microsoft (United States)
Redmond, United States
17 h-index
76 publications
10.1K citations
Google (United States)
Mountain View, United States
17 h-index
75 publications
11.4K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
12 h-index
52 publications
9.6K citations
Tsinghua University
Beijing, China
12 h-index
45 publications
3.2K citations

Chinese Academy of Sciences
Beijing, China
10 h-index
35 publications
1.3K citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
26 publications
4.3K citations
Stanford University
Stanford, United States
10 h-index
21 publications
2.7K citations
University of Washington
Seattle, United States
10 h-index
28 publications
2.4K citations
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Repository · 122 publications · 7.8K citations
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Conference · 12 publications · 549 citations
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Conference · 12 publications · 1.6K citations
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Conference · 11 publications · 1.1K citations
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Repository · 7 publications · 558 citations
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