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 981–990 of 2226 papers

TitleStatusHype
FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing—0
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble—0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms—0
A time-varying finance-led model for U.S. business cycles—0
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering—0
Federated Variational Inference: Towards Improved Personalization and Generalization—0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
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Benchmark Results

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