8.1k citations · 11.5k across the 25 of their papers we have counts for
6 papers · 1 filter
POPE: Post Optimization Posterior Evaluation of Likelihood Free Models
Edward Meeds, Michael Chiang, Mary Lee +3
In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stoc…
MLitB: Machine Learning in the Browser
Edward Meeds, Remco Hendriks, Said Al Faraby +2
With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has v…
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…
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
Yutian Chen, Max Welling
In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1…
Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed +1
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practi…
Exploiting the Statistics of Learning and Inference
Max Welling
When dealing with datasets containing a billion instances or with simulations that require a supercomputer to execute, computational resources become part of the equation. We can i…