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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 1426–1450 of 2226 papers

TitleStatusHype
Bayesian ODE Solvers: The Maximum A Posteriori Estimate—0
A Bayesian brain model of adaptive behavior: An application to the Wisconsin Card Sorting Task—0
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of componentsCode0
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
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
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks—0
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging—0
How Good is the Bayes Posterior in Deep Neural Networks Really?—0
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications—0
Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control—0
Transport Gaussian Processes for Regression—0
The Case for Bayesian Deep Learning—0
Bayesian Reasoning with Trained Neural Networks—0
Heterogeneous Learning from Demonstration—0
The Reciprocal Bayesian LASSOCode0
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Benchmark Results

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