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 1061–1070 of 2226 papers

TitleStatusHype
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—0
Probabilistic semi-nonnegative matrix factorization: a Skellam-based frameworkCode1
Biases and Variability from Costly Bayesian Inference—0
InfoNCE is variational inference in a recognition parameterised model—0
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors—0
Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistence—0
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on GraphsCode0
q-Paths: Generalizing the Geometric Annealing Path using Power MeansCode0
Applications of the Free Energy Principle to Machine Learning and Neuroscience—0
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence—0
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Benchmark Results

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