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 311–320 of 2226 papers

TitleStatusHype
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural NetworksCode0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
How To Make Your Cell Tracker Say "I dunno!"—0
Efficient Membership Inference Attacks by Bayesian Neural Network—0
HIF: Height Interval Filtering for Efficient Dynamic Points Removal—0
Poisoning Bayesian Inference via Data Deletion and Replication—0
Optimal Power Management for Large-Scale Battery Energy Storage Systems via Bayesian Inference—0
Correcting Mode Proportion Bias in Generalized Bayesian Inference via a Weighted Kernel Stein Discrepancy—0
From Understanding the World to Intervening in It: A Unified Multi-Scale Framework for Embodied CognitionCode0
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Benchmark Results

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