SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 871–880 of 2226 papers

TitleStatusHype
Efficient Membership Inference Attacks by Bayesian Neural Network—0
Expectation Propagation performs a smoothed gradient descent—0
Bayesian Inference in Model-Based Machine Vision—0
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline—0
Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs—0
Bayesian Inference in Physics-Driven Problems with Adversarial Priors—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks—0
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified