Results 11 to 20 of about 5,051,769 (292)
GFlowNets and variational inference [PDF]
CoRR, 2022 This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used ...
Nikolay Malkin +7 more
semanticscholar +4 more sources
Sliced Wasserstein Variational Inference [PDF]
CoRR, 2022 Variational Inference approximates an unnormalized distribution via the minimization of Kullback-Leibler (KL) divergence. Although this divergence is efficient for computation and has been widely used in applications, it suffers from some unreasonable ...
Mingxuan Yi, Song Liu
semanticscholar +5 more sources
Variational inference with a quantum computer [PDF]
Physical Review Applied, 2021 Inference is the task of drawing conclusions about unobserved variables given observations of related variables. Applications range from identifying diseases from symptoms to classifying economic regimes from price movements.
Marcello Benedetti +4 more
semanticscholar +4 more sources
Advances in Variational Inference [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference.
Cheng Zhang +3 more
semanticscholar +5 more sources
Langevin Diffusion Variational Inference [PDF]
CoRR, 2022 Many methods that build powerful variational distributions based on unadjusted Langevin transitions exist. Most of these were developed using a wide range of different approaches and techniques.
Tomas Geffner, Justin Domke
semanticscholar +4 more sources
Black Box Variational Inference [PDF]
CoRR, 2013 Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally requires significant model-specific analysis, and these efforts can hinder ...
Blei, David M. +2 more
core +5 more sources
On the approximation accuracy of Gaussian variational inference [PDF]
Annals of Statistics, 2023 The main computational challenge in Bayesian inference is to compute integrals against a high-dimensional posterior distribution. In the past decades, variational inference (VI) has emerged as a tractable approximation to these integrals, and a viable ...
A. Katsevich, P. Rigollet
semanticscholar +1 more source
Variational inference via Wasserstein gradient flows [PDF]
Neural Information Processing Systems, 2022 Along with Markov chain Monte Carlo (MCMC) methods, variational inference (VI) has emerged as a central computational approach to large-scale Bayesian inference.
Marc Lambert +4 more
semanticscholar +1 more source
Amortized Variational Inference: When and Why? [PDF]
Conference on Uncertainty in Artificial Intelligence, 2023 In a probabilistic latent variable model, factorized (or mean-field) variational inference (F-VI) fits a separate parametric distribution for each latent variable.
Charles C. Margossian, D. Blei
semanticscholar +1 more source
Overview of Research on Bayesian Inference and Parallel Tempering [PDF]
Jisuanji kexue, 2023 Bayesian inference is one of the main problems in statistics.It aims to update the prior knowledge of the probability distribution model based on the observation data.For the posterior probability that cannot be observed or is difficult to directly ...
ZHAN Jin, WANG Xuefei, CHENG Yurong, YUAN Ye
doaj +1 more source