358 citations · 613 across the 4 of their papers we have counts for
4 papers
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…
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik P. Kingma, Max Welling
Recent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary rand…