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 1541–1550 of 2226 papers

TitleStatusHype
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping—0
Improved algorithm for neuronal ensemble inference by Monte Carlo method—0
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation—0
Improved Combinatory Categorial Grammar Induction with Boundary Words and Bayesian Inference—0
Improved Neuronal Ensemble Inference with Generative Model and MCMC—0
Improvement and generalization of ABCD method with Bayesian inference—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators—0
Structured Dropout Variational Inference for Bayesian Neural Networks—0
Improving Generalization with Flat Hilbert Bayesian Inference—0
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Benchmark Results

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