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Found 6 papers, 2 papers with code
VISA: Variational Inference with Sequential Sample-Average Approximations
We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on numerical simulations.
Topological Obstructions and How to Avoid Them
Incorporating geometric inductive biases into models can aid interpretability and generalization, but encoding to a specific geometric structure can be challenging due to the imposed topological constraints.
A Variational Perspective on Generative Flow Networks
Generative flow networks (GFNs) are a class of models for sequential sampling of composite objects, which approximate a target distribution that is defined in terms of an energy function or a reward.
Nested Variational Inference
We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting.
Learning Proposals for Probabilistic Programs with Inference Combinators
Proposals in these samplers can be parameterized using neural networks, which in turn can be trained by optimizing variational objectives.
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling.
