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Bayesian Inference

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1621–1630 of 2226 papers

Title	Date	Tasks	Status	Hype
Decentralized Bayesian Learning over Graphs	May 24, 2019	Bayesian InferenceFederated Learning	—Unverified	0
Estimating Risk and Uncertainty in Deep Reinforcement Learning	May 23, 2019	Bayesian InferenceDeep Reinforcement Learning	CodeCode Available	0
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation	May 23, 2019	Bayesian InferenceData Augmentation	CodeCode Available	0
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting	May 23, 2019	Bayesian Inference	—Unverified	0
Accelerating Langevin Sampling with Birth-death	May 23, 2019	Bayesian Inference	—Unverified	0
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk	May 21, 2019	Bayesian InferenceBenchmarking	CodeCode Available	0
Exploring helical dynamos with machine learning	May 20, 2019	Bayesian InferenceBIG-bench Machine Learning	CodeCode Available	0
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations	May 17, 2019	Bayesian InferenceVocal Bursts Intensity Prediction	—Unverified	0
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference	May 14, 2019	Bayesian InferenceCompressive Sensing	—Unverified	0
Variational approximations using Fisher divergence	May 13, 2019	Bayesian Inference	—Unverified	0

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	F-SWA	Accuracy	83.61	—	Unverified
2	F-SWAG	Accuracy	80.93	—	Unverified