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 1681–1690 of 2226 papers

TitleStatusHype
Mean-field Variational Inference via Wasserstein Gradient Flow—0
Merging MCMC Subposteriors through Gaussian-Process Approximations—0
Message Passing Stein Variational Gradient Descent—0
Meta-Learning for Variational Inference—0
Meta-Learning Divergences of Variational Inference—0
Metropolis-CVAE: Bootstrapping Labels for Bayesian Inference via Semi-Supervised Conditional Variational Autoencoders—0
Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study—0
Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference—0
Metropolis-Hastings view on variational inference and adversarial training—0
Metropolis Sampling—0
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Benchmark Results

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