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Found 5 papers, 3 papers with code
Recursive Monte Carlo and Variational Inference with Auxiliary Variables
A key design constraint when implementing Monte Carlo and variational inference algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities of proposal distributions and variational families.
Estimators of Entropy and Information via Inference in Probabilistic Models
Estimating information-theoretic quantities such as entropy and mutual information is central to many problems in statistics and machine learning, but challenging in high dimensions.
3DP3: 3D Scene Perception via Probabilistic Programming
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images.
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models
Open-universe probabilistic models enable Bayesian inference about how many objects underlie data, and how they are related.
Automating Involutive MCMC using Probabilistic and Differentiable Programming
Involutive MCMC is a unifying mathematical construction for MCMC kernels that generalizes many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels based on deep neural networks.
Computation
