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 521–530 of 2226 papers

TitleStatusHype
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks—0
Bayesian inference of composition-dependent phase diagrams—0
PAVI: Plate-Amortized Variational Inference—0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Deep Learning and Bayesian inference for Inverse Problems—0
A transport approach to sequential simulation-based inference—0
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
On Exact Bayesian Credible Sets for Classification and Pattern Recognition—0
Linking fast and slow: the case for generative models—0
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Benchmark Results

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