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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 1551–1560 of 2226 papers

Title	Date	Tasks	Status	Hype
GAN priors for Bayesian inference	Sep 14, 2019	Bayesian InferenceGenerative Adversarial Network	—Unverified	0
Adversarial α-divergence Minimization for Bayesian Approximate Inference	Sep 13, 2019	Bayesian Inference	CodeCode Available	0
Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy	Sep 13, 2019	AstronomyBayesian Inference	CodeCode Available	0
Learning Bayesian posteriors with neural networks for gravitational-wave inference	Sep 12, 2019	AstronomyBayesian Inference	CodeCode Available	0
Efficient Bayesian synthetic likelihood with whitening transformations	Sep 11, 2019	Bayesian Inference	—Unverified	0
Correcting Predictions for Approximate Bayesian Inference	Sep 11, 2019	Bayesian InferenceDecision Making	CodeCode Available	0
Spike Sorting using the Neural Clustering Process	Sep 11, 2019	Bayesian InferenceClustering	CodeCode Available	0
Evaluating Topic Quality with Posterior Variability	Sep 8, 2019	Bayesian InferenceTopic Models	CodeCode Available	0
Accelerated Information Gradient flow	Sep 4, 2019	Bayesian Inference	CodeCode Available	0
On the Expressiveness of Approximate Inference in Bayesian Neural Networks	Sep 2, 2019	Active LearningBayesian Inference	CodeCode Available	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