SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2201–2210 of 2226 papers

TitleStatusHype
Uncertainty-aware generative models for inferring document class prevalenceCode0
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Practical calibration of the temperature parameter in Gibbs posteriorsCode0
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational InferenceCode0
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filterCode0
Kernel Semi-Implicit Variational InferenceCode0
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularizationCode0
The Benefit of Being Bayesian in Online Conformal PredictionCode0
Bayesian Convolutional Neural Networks for Compressed Sensing RestorationCode0
Bayesian Prediction of Future Street Scenes using Synthetic LikelihoodsCode0
Show:102550
← PrevPage 221 of 223Next →

Benchmark Results

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