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 1211–1220 of 2226 papers

TitleStatusHype
Score Matched Neural Exponential Families for Likelihood-Free InferenceCode1
Forming Real-World Human-Robot Cooperation for Tasks With General Goal—0
Spacecraft Collision Risk Assessment with Probabilistic ProgrammingCode1
Lévy walks derived from a Bayesian decision-making model in non-stationary environments—0
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interactionCode0
Bayesian Neural Ordinary Differential Equations—0
Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian InferenceCode0
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent—0
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantificationCode1
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty RegularizationCode0
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Benchmark Results

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