358 citations · 613 across the 4 of their papers we have counts for
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Emerging Convolutions for Generative Normalizing Flows
Emiel Hoogeboom, Rianne van den Berg, Max Welling
Generative flows are attractive because they admit exact likelihood optimization and efficient image synthesis. Recently, Kingma & Dhariwal (2018) demonstrated with Glow that gener…
Bayesian Dark Knowledge
Anoop Korattikara, Vivek Rathod, Kevin Murphy +1
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accur…
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Edward Meeds, Max Welling
We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples gener…