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
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting—0
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference—0
Bayesian Inference for Large Scale Image Classification—0
Domain Agnostic Conditional Invariant Predictions for Domain Generalization—0
Bayesian Inference for Jump-Diffusion Approximations of Biochemical Reaction Networks—0
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference—0
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Benchmark Results

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