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 1341–1350 of 2226 papers

TitleStatusHype
Efficient Low-Order Approximation of First-Passage Time Distributions—0
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods—0
Efficient Membership Inference Attacks by Bayesian Neural Network—0
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline—0
Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning—0
EinSteinVI: General and Integrated Stein Variational Inference—0
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Benchmark Results

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