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Bayesian Inference

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

Papers

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Showing 981–990 of 2226 papers

TitleStatusHype
Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-trainingCode1
Kalman filters as the steady-state solution of gradient descent on variational free energy—0
Locally Learned Synaptic Dropout for Complete Bayesian InferenceCode1
The "Bayesian" brain, with a bit less Bayes—0
Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks—0
Bayesian inference of the climbing grade scale—0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
Multi-Task Neural Processes—0
Variational Multi-Task Learning with Gumbel-Softmax PriorsCode1
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified