7 citations · 21 across the 6 of their papers we have counts for
4 papers · 1 filter
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Hao Wu, Heiko Zimmermann, Eli Sennesh +2
We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling. APG samplers constru…
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
Tuan Anh Le, Adam R. Kosiorek, N. Siddharth +2
Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables. Amortized gradient-based learning of SCFMs…
Tighter Variational Bounds are Not Necessarily Better
Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le +4
We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the si…
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…