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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 2001–2010 of 2226 papers

TitleStatusHype
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian ProcessesCode0
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methodsCode0
Functional Ensemble DistillationCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Robustness Guarantees for Bayesian Inference with Gaussian ProcessesCode0
Stochastic Gradient MCMC with Repulsive ForcesCode0
Functional Variational Bayesian Neural NetworksCode0
Robust One Round Federated Learning with Predictive Space Bayesian InferenceCode0
Bayesian Inference on Brain-Computer Interfaces via GLASSCode0
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Benchmark Results

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