SOTAVerified

Bayesian Inference

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1576–1600 of 2226 papers

TitleStatusHype
A General Framework for Uncertainty Estimation in Deep LearningCode0
Stochastic gradient Markov chain Monte CarloCode0
Concentration of the matrix-valued minimum mean-square error in optimal Bayesian inference—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
Deep Active Inference as Variational Policy GradientsCode0
Thompson Sampling on Symmetric α-Stable Bandits—0
Variational Inference MPC for Bayesian Model-based Reinforcement Learning—0
Learning in Volatile Environments with the Bayes Factor Surprise—0
Probabilistic CCA with Implicit Distributions—0
Adaptive particle-based approximations of the Gibbs posterior for inverse problems—0
Bandit Learning for Diversified Interactive Recommendation—0
Teaching deep neural networks to localize single molecules for super-resolution microscopyCode0
Parameter Estimation and Uncertainty Quantification for Systems Biology Models—0
Bayesian Inference of Spacecraft Pose using Particle Filtering—0
Probabilistic model predictive safety certification for learning-based control—0
Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural NetworksCode0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Reweighted Expectation MaximizationCode0
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical BayesCode0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
Approximate Variational Inference Based on a Finite Sample of Gaussian Latent VariablesCode0
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models—0
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep NetworksCode0
Show:102550
← PrevPage 64 of 90Next →

Benchmark Results

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