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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1641–1650 of 2226 papers

TitleStatusHype
Bayesian Convolutional Neural Networks for Compressed Sensing RestorationCode0
Gaussian Process Priors for Dynamic Paired Comparison ModellingCode0
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPsCode0
Bayesian Online Prediction of Change PointsCode0
Manifold Optimization Assisted Gaussian Variational Approximation—0
Cyclical Stochastic Gradient MCMC for Bayesian Deep LearningCode1
A stochastic version of Stein Variational Gradient Descent for efficient sampling—0
Low-pass filtering as Bayesian inference—0
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior BootstrapCode0
A Simple Baseline for Bayesian Uncertainty in Deep LearningCode1
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Benchmark Results

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