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Bayesian Inference

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

Papers

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Showing 101–125 of 2226 papers

TitleStatusHype
Enriching ImageNet with Human Similarity Judgments and Psychological EmbeddingsCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
A Bayesian algorithm for retrosynthesisCode1
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian InferenceCode1
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel RecombinationCode1
A Probabilistic State Space Model for Joint Inference from Differential Equations and DataCode1
ForecastPFN: Synthetically-Trained Zero-Shot ForecastingCode1
From Theory to Application: A Practical Introduction to Neural Operators in Scientific ComputingCode1
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variableCode1
A Simple Baseline for Bayesian Uncertainty in Deep LearningCode1
Gaussian process learning of nonlinear dynamicsCode1
Inferring COVID-19 spreading rates and potential change points for case number forecastsCode1
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte CarloCode1
Learning by example: fast reliability-aware seismic imaging with normalizing flowsCode1
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided PruningCode1
Lifelong Incremental Reinforcement Learning with Online Bayesian InferenceCode1
Listening to the Noise: Blind Denoising with Gibbs DiffusionCode1
BayesDLL: Bayesian Deep Learning LibraryCode1
A friendly introduction to triangular transportCode1
Particle Flow Bayes' RuleCode1
Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World SurveillanceCode1
Monte Carlo guided Diffusion for Bayesian linear inverse problemsCode1
Multi-marginal optimal transport and probabilistic graphical modelsCode1
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric ApproachesCode1
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Benchmark Results

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