Stanford CS236: Deep Generative Models I 2023 I Lecture 11 - Energy Based Models

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Stefano Ermon
Associate Professor of Computer Science, Stanford University
cs.stanford.edu/~ermon/
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28:45 Autoregressive models, latent variable models thought of as clever ways of combining simple normalized objects and building more complicated normalized objects by design 43:00 Sampling is hard, even though the likelihood is known, because of the normalizing constant (numeric not analytical methods) 49:20 Applications without the partition function 51:50? Uncorrupting image, maximize p(y|x) or equivalently maximize p(x,y), normalization constant doesn't matter 1:00:00? RBM 1:17:40 Contrastive divergence algorithm is a Monte Carlo approximation (single sample) of the expectation, unbiased estimator of the true gradient 1:23:20 MCMC for sample generation without partition function, Metropolis Hastings, downhill move with probability of taking that move