no code implementations • ICML 2020 • Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation.
no code implementations • 14 Apr 2023 • Evgenii Egorov, Roberto Bondesan, Max Welling
Quantum error correction is a critical component for scaling up quantum computing.
no code implementations • 30 Jun 2020 • Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation.
1 code implementation • MIDL 2019 • Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods.
no code implementations • pproximateinference AABI Symposium 2019 • Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
Variational Auto Encoders (VAE) are capable of generating realistic images, sounds and video sequences.
1 code implementation • NeurIPS 2021 • Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
We learn the approximation of the aggregated posterior as a prior for each task.
no code implementations • 15 Aug 2019 • Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods.
1 code implementation • NeurIPS 2019 • Kirill Neklyudov, Evgenii Egorov, Dmitry Vetrov
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.
no code implementations • 20 May 2019 • Evgenii Egorov, Kirill Neklydov, Ruslan Kostoev, Evgeny Burnaev
One of the core problems in variational inference is a choice of approximate posterior distribution.
no code implementations • ICLR 2019 • Kirill Neklyudov, Evgenii Egorov, Pavel Shvechikov, Dmitry Vetrov
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?