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 2111–2120 of 2226 papers

TitleStatusHype
Surrogate-assisted Bayesian inversion for landscape and basin evolution modelsCode0
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free InferenceCode0
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex OptimizationCode0
Surrogate-assisted parallel tempering for Bayesian neural learningCode0
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss InformationCode0
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural NetworksCode0
Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network ApproachCode0
Swift sky localization of gravitational waves using deep learning seeded importance samplingCode0
VKFPos: A Learning-Based Monocular Positioning with Variational Bayesian Extended Kalman Filter IntegrationCode0
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Benchmark Results

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