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 2021–2030 of 2226 papers

TitleStatusHype
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Stable neural networks: large-width asymptotics and convergence rates—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Density Estimation via Bayesian Inference Engines—0
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation—0
De-randomizing MCMC dynamics with the diffusion Stein operator—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
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Benchmark Results

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