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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 1391–1400 of 2226 papers

TitleStatusHype
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning—0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development—0
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning—0
Uncertainty propagation in neural networks for sparse coding—0
Uncertainty quantification in covid-19 spread: lockdown effects—0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective—0
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Benchmark Results

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