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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 1571–1580 of 2226 papers

TitleStatusHype
Probabilistic model predictive safety certification for learning-based control—0
Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural NetworksCode0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Reweighted Expectation MaximizationCode0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical BayesCode0
Approximate Variational Inference Based on a Finite Sample of Gaussian Latent VariablesCode0
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models—0
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep NetworksCode0
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family ApproximationsCode0
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Benchmark Results

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