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 1901–1910 of 2226 papers

TitleStatusHype
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series—0
Bidirectional Recurrent Neural Networks as Generative Models—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Big Learning with Bayesian Methods—0
Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems—0
Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon—0
Biologically Inspired Dynamic Textures for Probing Motion Perception—0
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks—0
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Benchmark Results

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