71 citations · 243 across the 20 of their papers we have counts for
4 papers · 1 filter
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio +8
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from po…
Bayesian Optimization for Probabilistic Programs
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent +2
We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evide…
Auto-Encoding Sequential Monte Carlo
Tuan Anh Le, Maximilian Igl, Tom Rainforth +2
We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad fam…
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov +1
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a…