Probabilistic inference of Bayesian neural networks with generalized expectation propagation
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.060
Abstract: Abstract Deep learning plays an important role in the field of machine learning. However, deterministic methods such as neural networks cannot capture the model uncertainty. Bayesian neural network (BNN) are recently under consideration since Bayesian… read more here.
Keywords: neural networks; approximate inference; method; expectation propagation ... See more keywords
Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Computational and Graphical Statistics"
DOI: 10.1080/10618600.2021.1880921
Abstract: A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under continuity assumptions, quantiles of functions of the simulated parameter… read more here.
Keywords: adjustment approximate; law total; approximate inference; total variance ... See more keywords
Analysis of random sequential message passing algorithms for approximate inference
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Statistical Mechanics: Theory and Experiment"
DOI: 10.1088/1742-5468/ac764a
Abstract: We analyze the dynamics of a random sequential message passing algorithm for approximate inference with large Gaussian latent variable models in a student–teacher scenario. To model nontrivial dependencies between the latent variables, we assume random… read more here.
Keywords: random sequential; message passing; sequential message; model ... See more keywords
Probabilistic MIMO Symbol Detection With Expectation Consistency Approximate Inference
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2017.2786638
Abstract: In this paper, we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel algorithm based on the expectation consistency (EC) framework, which describes the approximate inference… read more here.
Keywords: approximate inference; symbol detection; expectation consistency; symbol ... See more keywords