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Found 10 papers, 3 papers with code
Involutive MCMC: One Way to Derive Them All
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation.
The END: An Equivariant Neural Decoder for Quantum Error Correction
Quantum error correction is a critical component for scaling up quantum computing.
Involutive MCMC: a Unifying Framework
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation.
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods.
BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach
Variational Auto Encoders (VAE) are capable of generating realistic images, sounds and video sequences.
BooVAE: Boosting Approach for Continual Learning of VAE
We learn the approximation of the aggregated posterior as a prior for each task.
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods.
The Implicit Metropolis-Hastings Algorithm
For any implicit probabilistic model and a target distribution represented by a set of samples, implicit Metropolis-Hastings operates by learning a discriminator to estimate the density-ratio and then generating a chain of samples.
MaxEntropy Pursuit Variational Inference
One of the core problems in variational inference is a choice of approximate posterior distribution.
Metropolis-Hastings view on variational inference and adversarial training
From this point of view, the problem of constructing a sampler can be reduced to the question - how to choose a proposal for the MH algorithm?
