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 1431–1440 of 2226 papers

TitleStatusHype
Deep Active Inference for Autonomous Robot Navigation—0
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
Fast Convergence for Langevin with Matrix Manifold Structure—0
Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations—0
Generalized Bayesian Filtering via Sequential Monte Carlo—0
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization—0
Sequential Cooperative Bayesian Inference—0
Fast Convergence for Langevin Diffusion with Manifold Structure—0
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Benchmark Results

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