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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 971–980 of 2226 papers

TitleStatusHype
Sampling with Trusthworthy Constraints: A Variational Gradient FrameworkCode0
Functional Variational Inference based on Stochastic Process Generators—0
A category theory framework for Bayesian learning—0
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian InferenceCode0
Forget-SVGD: Particle-Based Bayesian Federated Unlearning—0
Probabilistic Deep Learning with Generalised Variational Inference—0
Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach—0
Can Sequential Bayesian Inference Solve Continual Learning?—0
Bridging the reality gap in quantum devices with physics-aware machine learning—0
Bayesian Learning via Neural Schrödinger-Föllmer Flows—0
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Benchmark Results

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