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 1481–1490 of 2226 papers

TitleStatusHype
Generative Modeling: A Review—0
Generative models and Bayesian inversion using Laplace approximation—0
Accelerated physical emulation of Bayesian inference in spiking neural networks—0
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models—0
Geometric Ergodicity in Modified Variations of Riemannian Manifold and Lagrangian Monte Carlo—0
Geometry of Score Based Generative Models—0
GFlowOut: Dropout with Generative Flow Networks—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Global seismic monitoring as probabilistic inference—0
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory—0
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Benchmark Results

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