SOTAVerified

Bayesian Inference

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

Papers

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Showing 1391–1400 of 2226 papers

TitleStatusHype
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of componentsCode0
BayesFlow: Learning complex stochastic models with invertible neural networksCode1
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods—0
The Variational InfoMax Learning Objective—0
Deep Active Inference for Autonomous Robot Navigation—0
A Bayesian algorithm for retrosynthesisCode1
Flexible Bayesian Nonlinear Model ConfigurationCode1
MPC-guided Imitation Learning of Neural Network Policies for the Artificial PancreasCode0
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference—0
MC^2RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference—0
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Benchmark Results

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