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 91–100 of 2226 papers

TitleStatusHype
E-PINNs: Epistemic Physics-Informed Neural Networks—0
AutoBayes: A Compositional Framework for Generalized Variational Inference—0
Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models—0
SuperARC: An Agnostic Test for Narrow, General, and Super Intelligence Based On the Principles of Recursive Compression and Algorithmic ProbabilityCode0
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble—0
Simulation-based Bayesian inference under model misspecification—0
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural NetworksCode0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
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Benchmark Results

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