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 231–240 of 2226 papers

TitleStatusHype
On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization GuaranteesCode0
Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform InversionCode0
Goal Inference from Open-Ended Dialog—0
Active inference and deep generative modeling for cognitive ultrasound—0
Bayesian inference of mixed Gaussian phylogenetic modelsCode0
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation MatrixCode0
Optimal lower bounds for logistic log-likelihoods—0
Efficient Reinforcement Learning with Large Language Model Priors—0
A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension—0
On the Minimal Theory of Consciousness Implicit in Active Inference—0
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Benchmark Results

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