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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 691–700 of 2226 papers

Title	Date	Tasks	Status	Hype	Score
Learning to infer in recurrent biological networks	Jun 18, 2020	Bayesian InferenceVariational Inference	CodeCode Available	0	5
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction	Dec 15, 2020	Bayesian InferenceDeep Learning	CodeCode Available	0	5
A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning	Jun 16, 2023	Bayesian InferenceMeta-Learning	CodeCode Available	0	5
Constrained Sampling with Primal-Dual Langevin Monte Carlo	Nov 1, 2024	Bayesian Inferencecounterfactual	CodeCode Available	0	5
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural network	May 18, 2022	Bayesian InferenceImage Deconvolution	CodeCode Available	0	5
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms	May 25, 2023	Bayesian InferenceImage Deconvolution	CodeCode Available	0	5
Normalizing Constant Estimation with Gaussianized Bridge Sampling	Dec 12, 2019	Bayesian Inference	CodeCode Available	0	5
deBInfer: Bayesian inference for dynamical models of biological systems in R	Oct 15, 2016	Bayesian Inference	CodeCode Available	0	5
Continual Multi-task Gaussian Processes	Oct 31, 2019	Bayesian InferenceContinual Learning	CodeCode Available	0	5
Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study	Feb 2, 2021	Bayesian InferenceVariational Inference	CodeCode Available	0	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