Multimodal Variational Inference
Multimodal Variational Inference is a variational inference framework that extends standard variational methods by modeling complex, multimodal posterior distributions through flexible approximation families capable of capturing dependencies across multiple distinct data modalities.
38
Publications
2.2K
Citations
164
Authors
80
Institutions
Publications per year
2017–2026
34
Parents
38
MVAE: Multimodal Variational Autoencoder for Fake News Detection
Dhruv Khattar, Jaipal Singh Goud, Manish Gupta et al. · 2019 · 745 citations · Full text
The covariance environment defines cellular niches for spatial inference
Doron Haviv, Ján Remšík, Mohamed I. Gatie et al. · Nature Biotechnology · 2024 · 57 citations · Full text
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National University of Singapore
Singapore, Singapore
3 h-index
4 publications
85 citations
Memorial Sloan Kettering Cancer Center
New York, United States
2 h-index
15 publications
650 citations

Howard Hughes Medical Institute
Chevy Chase, United States
2 h-index
2 publications
73 citations
Massachusetts Institute of Technology
Cambridge, United States
2 h-index
2 publications
71 citations
Weill Cornell Medicine
New York, United States
2 h-index
2 publications
73 citations

Delft University of Technology
Delft, The Netherlands
2 h-index
7 publications
156 citations
Iowa State University
Ames, United States
2 h-index
3 publications
129 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
2 h-index
4 publications
1.2K citations
1–8 of 80
Repository · 6 publications · 101 citations
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Conference · 2 publications · 41 citations
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Conference · 2 publications · 60 citations
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1 publications · 23 citations
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Conference · 1 publications · 51 citations
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