SOTAVerified

Bayesian Inference

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

Papers

Showing 10511075 of 2226 papers

TitleStatusHype
EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models0
Structured Stochastic Gradient MCMCCode0
Mismatched Estimation of rank-one symmetric matrices under Gaussian noiseCode0
Compressed Monte Carlo with application in particle filtering0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers0
SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All NetworksCode1
Bayesian brains and the Rényi divergence0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty0
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential EquationsCode0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks0
Biases and Variability from Costly Bayesian Inference0
Probabilistic semi-nonnegative matrix factorization: a Skellam-based frameworkCode1
InfoNCE is variational inference in a recognition parameterised model0
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors0
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on GraphsCode0
Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistence0
q-Paths: Generalizing the Geometric Annealing Path using Power MeansCode0
Applications of the Free Energy Principle to Machine Learning and Neuroscience0
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence0
AutoEKF: Scalable System Identification for COVID-19 Forecasting from Large-Scale GPS Data0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model0
Bayesian Eye Tracking0
Bayesian Neural Networks: Essentials0
Repulsive Deep Ensembles are BayesianCode1
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Benchmark Results

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