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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 1511–1520 of 2226 papers

TitleStatusHype
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational InferenceCode1
Debiased Bayesian inference for average treatment effectsCode0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex—0
Meta-Learning for Variational Inference—0
Mixture Distributions for Scalable Bayesian Inference—0
Learning Curves for Deep Neural Networks: A field theory perspective—0
Gaussian Process Meta-Representations Of Neural Networks—0
On the Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks—0
Quantifying uncertainty with GAN-based priors—0
Refining the variational posterior through iterative optimization—0
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Benchmark Results

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