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 2011–2020 of 2226 papers

TitleStatusHype
Deep de Finetti: Recovering Topic Distributions from Large Language Models—0
Deep Ensemble as a Gaussian Process Approximate Posterior—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Deep Knowledge Tracing with Learning Curves—0
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems—0
Deep Learning and Bayesian inference for Inverse Problems—0
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI—0
Deep Learning Surrogates for Real-Time Gas Emission Inversion—0
Deep Maxout Network Gaussian Process—0
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Benchmark Results

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