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 2031–2040 of 2226 papers

TitleStatusHype
A Theoretically Grounded Application of Dropout in Recurrent Neural NetworksCode0
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural networkCode0
Generalised Bayesian distance-based phylogenetics for the genomics eraCode0
A Bayesian Approach for Medical Inquiry and Disease Inference in Automated Differential DiagnosisCode0
Generalised Bayesian Inference for Discrete Intractable LikelihoodCode0
Asynchronous and Distributed Data Augmentation for Massive Data SettingsCode0
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMsCode0
Stochastic Model of siRNA Endosomal Escape Mediated by Fusogenic Peptides in OVCAR-3Code0
Sampling-based inference for large linear models, with application to linearised LaplaceCode0
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo AlgorithmsCode0
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Benchmark Results

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