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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 191–200 of 2226 papers

Title	Date	Tasks	Status	Hype
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo	Oct 14, 2019	Bayesian Inference	CodeCode Available	1
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels	Oct 11, 2019	Bayesian InferenceDomain Adaptation	CodeCode Available	1
Validated Variational Inference via Practical Posterior Error Bounds	Oct 9, 2019	Bayesian InferenceVariational Inference	CodeCode Available	1
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference	Sep 30, 2019	Bayesian InferenceVariational Inference	CodeCode Available	1
Amortized Monte Carlo Integration	Jul 18, 2019	Bayesian Inference	CodeCode Available	1
Automatic Posterior Transformation for Likelihood-Free Inference	May 17, 2019	Bayesian InferenceTime Series	CodeCode Available	1
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning	Feb 11, 2019	Bayesian InferenceDeep Learning	CodeCode Available	1
A Simple Baseline for Bayesian Uncertainty in Deep Learning	Feb 7, 2019	Bayesian InferenceDeep Learning	CodeCode Available	1
Particle Flow Bayes' Rule	Feb 2, 2019	Bayesian InferenceMeta-Learning	CodeCode Available	1
Neural Clustering Processes	Dec 28, 2018	Bayesian InferenceClustering	CodeCode Available	1

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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