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 1361–1370 of 2226 papers

TitleStatusHype
Towards a Unified Framework for Sequential Decision Making—0
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification—0
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry—0
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies—0
Towards Machine Wald—0
Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior—0
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task—0
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling—0
Towards Scalable Bayesian Learning of Causal DAGs—0
Towards Unifying Perceptual Reasoning and Logical Reasoning—0
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Benchmark Results

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