Stanford CS236: Deep Generative Models I 2023 I Lecture 5 - VAEs

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Stefano Ermon
Associate Professor of Computer Science, Stanford University
cs.stanford.edu/~ermon/
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[@dohyun0047]

@56:38 쯤에 수업 분위기가 너무 좋네욤ㅋㅋㅋㅋ

[@CPTSMONSTER]

29:30 Infinite number of latent variables z 30:10 Finite gaussians, able to choose parameters arbitrarily, lookup tables 30:30 Infinite gaussians, not arbitrary, chosen by feeding z through neural network 39:30 Parameters of infinite gaussian model 40:30? Positive semi-definite covariance matrix 41:30? Latent variable represented by part of image obscured 50:00 Number of latent variables (binary variables, Bernoulli) 52:00 Naive Monte Carlo approximation of likelihood function for partially observable data 1:02:30? Modify learning objective to do semi-supervised learning 1:04:00 Importance sampling with Monte Carlo 1:07:00? Unbiased estimator, is q(z^(j)) supposed to be maximized? 1:09:00 Biased estimator when computing log-likelihood, proof by Jensen's inequality for concave functions (log is concave) 1:14:30 Summary, log p_theta(x) desired. Conditioned on latent variables z, if infinite Gaussians, then intractable. Do importance sampling with Monte Carlo. Base case k=1 shows biased estimator for log p_theta(x). Jensen's inequality yields ELBO. Optimize by choosing q. 1:17:00 KUBO and other techniques for upper bound, much trickier to get UB 1:18:40? Entropy and equality when q is posterior distribution 1:19:40? E step of EM algorithm 1:20:30? Loop when training? x to z and z to x