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 1411–1420 of 2226 papers

TitleStatusHype
Approximations in the homogeneous Ising model—0
Fast Variational Inference for Large-scale Internet Diagnosis—0
Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies—0
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
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms—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
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Benchmark Results

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