Amortised Learning by Wake-Sleep

22 Feb 2020Li K. WenliangTheodore MoskovitzHeishiro KanagawaManeesh Sahani

Models that employ latent variables to capture structure in observed data lie at the heart of many current unsupervised learning algorithms, but exact maximum-likelihood learning for powerful and flexible latent-variable models is almost always intractable. Thus, state-of-the-art approaches either abandon the maximum-likelihood framework entirely, or else rely on a variety of variational approximations to the posterior distribution over the latents... (read more)

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