
Unnormalized Variational Bayes
We unify empirical Bayes and variational Bayes for approximating unnorma...
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Learning and Inference in Imaginary Noise Models
Inspired by recent developments in learning smoothed densities with empi...
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Provable Robust Classification via Learned Smoothed Densities
Smoothing classifiers and probability density functions with Gaussian ke...
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On approximating ∇ f with neural networks
Consider a feedforward neural network ψ: R^d→R^d such that ψ≈∇ f, where ...
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Neural Empirical Bayes
We formulate a novel framework that unifies kernel density estimation an...
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Deep Energy Estimator Networks
Density estimation is a fundamental problem in statistical learning. Thi...
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Annealed Generative Adversarial Networks
We introduce a novel framework for adversarial training where the target...
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The Wilson Machine for Image Modeling
Learning the distribution of natural images is one of the hardest and mo...
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Saeed Saremi
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