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 1711–1720 of 2226 papers

TitleStatusHype
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution—0
A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension—0
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM—0
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM—0
A Variational View on Bootstrap Ensembles as Bayesian Inference—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty—0
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)—0
Bandit Learning for Diversified Interactive Recommendation—0
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Benchmark Results

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