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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 621–630 of 2226 papers

TitleStatusHype
Variational Inference for Bayesian Neural Networks under Model and Parameter UncertaintyCode0
Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)—0
Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems—0
Machine Learning and the Future of Bayesian Computation—0
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferenceCode1
Individual Fairness in Bayesian Neural NetworksCode0
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity—0
Martingale Posterior Neural Processes—0
A Latent Space Theory for Emergent Abilities in Large Language Models—0
Playing it safe: information constrains collective betting strategies—0
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Benchmark Results

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