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 691–700 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
Variational Density Propagation Continual Learning—0
Linking fast and slow: the case for generative models—0
On Exact Bayesian Credible Sets for Classification and Pattern Recognition—0
On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental DesignCode0
Semi-Implicit Variational Inference via Score MatchingCode0
Accelerated Bayesian imaging by relaxed proximal-point Langevin samplingCode0
Bayesian polynomial neural networks and polynomial neural ordinary differential equations—0
Adaptive mitigation of time-varying quantum noise—0
Natural Evolution Strategies as a Black Box Estimator for Stochastic Variational Inference—0
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Benchmark Results

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