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 1401–1410 of 2226 papers

TitleStatusHype
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior—0
Understanding and mitigating difficulties in posterior predictive evaluation—0
Understanding Approximation for Bayesian Inference in Neural Networks—0
Understanding Task Representations in Neural Networks via Bayesian Ablation—0
Unified field theoretical approach to deep and recurrent neuronal networks—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift—0
Unrolled denoising networks provably learn optimal Bayesian inference—0
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Benchmark Results

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