Variational Em Algorithm
Variational EM Algorithm is a variational inference method that iteratively optimizes a lower bound on the marginal likelihood by alternating between updating the variational distribution over latent variables (E-step) and updating the model parameters (M-step) using a mean-field approximation.
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190
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2017–2026
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Hagai Attias · Neural Computation · 1999 · 508 citations
Latent class models for collaborative filtering
Thomas Hofmann, Jan Puzicha · 1999 · 420 citations
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Centre National de la Recherche Scientifique
Paris, France
8 h-index
14 publications
390 citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
17 publications
1.7K citations

City University of Hong Kong
Hong Kong, Hong Kong
5 h-index
11 publications
299 citations
Oxford Centre for Computational Neuroscience
Oxford, United Kingdom
4 h-index
6 publications
234 citations
University College London
London, United Kingdom
4 h-index
6 publications
268 citations
University of California, Berkeley
Berkeley, United States
4 h-index
11 publications
799 citations
National University of Singapore
Singapore, Singapore
4 h-index
7 publications
254 citations
University of California, Irvine
Irvine, United States
4 h-index
8 publications
246 citations
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Repository · 21 publications · 957 citations
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TPAMI · 6 publications · 3.1K citations
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AOAS · 4 publications · 149 citations
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