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 321–330 of 2226 papers

TitleStatusHype
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Bayesian geoacoustic inversion using mixture density network—0
A New Parameterized Family of Stochastic Particle Flow Filters—0
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination—0
A Neural Implementation of the Kalman Filter—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models—0
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
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Benchmark Results

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