Variational Inference: Foundations and Innovations

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David Blei, Columbia University
Computational Challenges in Machine Learning
  1. Simons Institute
  2. Simons Institute
  3. Theory of Computing
  4. Theory of Computation
  5. Theoretical Computer Science
  6. Computer Science
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Комментарии
Автор

Wonderful Talk~~~~
7:45 start
10:49 GMM model example
13:37 LDA example
22:42 Conditionally conjugate models
28:22 ELBO
30:52 Mean-field VI
37:27 Stochastic VI
48:07 Black box VI
1:00:47 Reparameterization and amortization

whaleshark
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“Great question! I wish this talk was over so I could go and think about it”

pauloabelha
Автор

Check Blei's latest talk on this topic:

And the 2016 NIPS tutorial talk:

bnglr
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amazing! brief presentation but gives deep insights

jiachenlei
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I understand we measure the distance between two distributions using KL divergence, but am still very confused. How do we know whether we are getting closer to the actual posterior distribution if we do not know the posterior distribution?

monart
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28:08 On the "bad properties of KL divergence" and alternative measures of divergence;
Does anyone have any things to point to? Very interesting

ewfq
Автор

Can we have access to the slides please?

martindelgado