Generative Modeling
Generative Modeling is a machine learning paradigm that belongs to the class of statistical modeling approaches, distinguished by its objective of learning the underlying probability distribution of data in order to generate new, synthetically similar instances.
12K
Publications
1.1M
Citations
34.9K
Authors
4.3K
Institutions
Publications per year
2017–2026
10.3K
12K
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu, Taesung Park, Phillip Isola et al. · 2017 · 21.9K citations · Full text
A Fast Learning Algorithm for Deep Belief Nets
Geoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh · Neural Computation · 2006 · 16.4K citations
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza et al. · Communications of the ACM · 2020 · 13.6K citations · Full text
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4.3K
Google (United States)
Mountain View, United States
89 h-index
791 publications
194.4K citations

University of Toronto
Toronto, Canada
78 h-index
344 publications
80.2K citations
Tsinghua University
Beijing, China
69 h-index
636 publications
44.5K citations
University of California, Berkeley
Berkeley, United States
67 h-index
364 publications
179.8K citations
Carnegie Mellon University
Pittsburgh, United States
67 h-index
451 publications
37.4K citations
Stanford University
Stanford, United States
65 h-index
452 publications
72.2K citations

Chinese Academy of Sciences
Beijing, China
64 h-index
788 publications
43.3K citations
Massachusetts Institute of Technology
Cambridge, United States
61 h-index
340 publications
49.2K citations
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Repository · 2.6K publications · 256.8K citations
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Conference · 236 publications · 18.3K citations
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TPAMI · 165 publications · 16.8K citations
62
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287 publications · 14.3K citations
61
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Conference · 84 publications · 22.8K citations
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