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 1141–1150 of 2226 papers

TitleStatusHype
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling—0
Towards Scalable Bayesian Learning of Causal DAGs—0
Towards Unifying Perceptual Reasoning and Logical Reasoning—0
Tracking disease outbreaks from sparse data with Bayesian inference—0
Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory—0
Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework—0
Trajectory Modeling via Random Utility Inverse Reinforcement Learning—0
Transflow Learning: Repurposing Flow Models Without Retraining—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Transport Gaussian Processes for Regression—0
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Benchmark Results

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