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Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary continuous random variable.
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Neural autoregressive flows
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Entropic gans meet vaes: A statistical approach to compute sample likelihoods in gans
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Leveraging exploration in off-policy algorithms via normalizing flows
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Neural empirical bayes
Saremi, S. and Hyvarinen, A · 2019
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Generative modeling by estimating gradients of the data distribution
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Sliced score matching: A scalable approach to density and score estimation
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Learning generative models using denoising density estimators
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Regularized autoencoders via relaxed injective probability flow
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Mutual information gradient estimation for representation learning
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