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 1981–1990 of 2226 papers

TitleStatusHype
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—0
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting—0
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties—0
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity—0
Copula Processes—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Correcting Mode Proportion Bias in Generalized Bayesian Inference via a Weighted Kernel Stein Discrepancy—0
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging—0
Cortical Microcircuits from a Generative Vision Model—0
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Benchmark Results

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