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 2191–2200 of 2226 papers

TitleStatusHype
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband ItemsCode0
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank DeterminationCode0
Population Empirical BayesCode0
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion ModelsCode0
Sequential transport maps using SoS density estimation and α-divergencesCode0
Neural Posterior Regularization for Likelihood-Free InferenceCode0
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte CarloCode0
Sharing deep generative representation for perceived image reconstruction from human brain activityCode0
Posterior SBC: Simulation-Based Calibration Checking Conditional on DataCode0
Variational Bayesian Bow tie Neural Networks with ShrinkageCode0
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Benchmark Results

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