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 1991–2000 of 2226 papers

TitleStatusHype
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators—0
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference—0
DART: Depth-Enhanced Accurate and Real-Time Background Matting—0
Data augmentation in Bayesian neural networks and the cold posterior effect—0
Data fission: splitting a single data point—0
Physics-constrained Bayesian inference of state functions in classical density-functional theory—0
Data-Driven Verification under Signal Temporal Logic Constraints—0
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems—0
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction—0
Decentralized Bayesian Learning over Graphs—0
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Benchmark Results

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