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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 111–120 of 2226 papers

Title	Date	Tasks	Status	Hype	Score
Bayesian hierarchical stacking: Some models are (somewhere) useful	Jan 22, 2021	Bayesian InferenceTime Series	CodeCode Available	1	5
Learning by example: fast reliability-aware seismic imaging with normalizing flows	Apr 13, 2021	Bayesian InferenceSeismic Imaging	CodeCode Available	1	5
Learning to Generalize across Domains on Single Test Samples	Feb 16, 2022	Bayesian InferenceDomain Adaptation	CodeCode Available	1	5
Lifelong Incremental Reinforcement Learning with Online Bayesian Inference	Jul 28, 2020	Bayesian InferenceClustering	CodeCode Available	1	5
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective	Jul 1, 2020	Bayesian Inference	CodeCode Available	1	5
Low-rank extended Kalman filtering for online learning of neural networks from streaming data	May 31, 2023	Bayesian InferenceVariational Inference	CodeCode Available	1	5
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks	Feb 24, 2020	Bayesian Inference	CodeCode Available	1	5
Meta-Learned Models of Cognition	Apr 12, 2023	Bayesian InferenceMeta-Learning	CodeCode Available	1	5
Bayesian differential programming for robust systems identification under uncertainty	Apr 15, 2020	Bayesian InferenceModel Discovery	CodeCode Available	1	5
Bayesian neural networks via MCMC: a Python-based tutorial	Apr 2, 2023	Bayesian InferenceDeep Learning	CodeCode Available	1	5

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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