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 71–80 of 2226 papers

TitleStatusHype
Robust Outlier Rejection for 3D Registration with Variational BayesCode1
Bayesian neural networks via MCMC: a Python-based tutorialCode1
Training Language Models with Language Feedback at ScaleCode1
PyVBMC: Efficient Bayesian inference in PythonCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetsCode1
Memory-Based Meta-Learning on Non-Stationary DistributionsCode1
QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative ModelsCode1
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time SeriesCode1
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Benchmark Results

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