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 811–820 of 2226 papers

TitleStatusHype
Bayesian Inference by Symbolic Model Checking—0
Efficient Membership Inference Attacks by Bayesian Neural Network—0
Distributed Bayesian inference for consistent labeling of tracked objects in non-overlapping camera networks—0
Distributed Bayesian Inference for Large-Scale IoT Systems—0
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks—0
Distributed Variational Bayesian Algorithms Over Sensor Networks—0
Distributionally Robust Optimisation with Bayesian Ambiguity Sets—0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity—0
Enhanced gradient-based MCMC in discrete spaces—0
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Benchmark Results

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