SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 2226 papers

TitleStatusHype
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Accounting for contact network uncertainty in epidemic inferences—0
Divide, Conquer, Combine Bayesian Decision Tree Sampling—0
A Unified Kernel for Neural Network Learning—0
Bridging the Sim-to-Real Gap with Bayesian Inference—0
Bridging Privacy and Robustness for Trustworthy Machine Learning—0
Predictive, scalable and interpretable knowledge tracing on structured domainsCode0
Clustered Mallows Model—0
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware PriorsCode0
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappingsCode0
Scalable Spatiotemporal Prediction with Bayesian Neural FieldsCode2
In-context Exploration-Exploitation for Reinforcement Learning—0
Bayesian Diffusion Models for 3D Shape ReconstructionCode1
Scalable Bayesian inference for the generalized linear mixed model—0
A prediction rigidity formalism for low-cost uncertainties in trained neural networks—0
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming—0
Statistical Mechanics of Dynamical System Identification—0
Listening to the Noise: Blind Denoising with Gibbs DiffusionCode1
Sequential transport maps using SoS density estimation and α-divergencesCode0
Demonstration of Robust and Efficient Quantum Property Learning with Shallow ShadowsCode2
Stochastic Approximation with Biased MCMC for Expectation MaximizationCode0
Quasi-Bayesian Estimation and Inference with Control Functions—0
Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification—0
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse PlanningCode1
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization—0
Show:102550
← PrevPage 16 of 90Next →

Benchmark Results

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