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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 1601–1650 of 2226 papers

TitleStatusHype
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting—0
Accelerating Langevin Sampling with Birth-death—0
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data AugmentationCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-RiskCode0
Exploring helical dynamos with machine learningCode0
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations—0
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference—0
Variational approximations using Fisher divergence—0
Spectral Reconstruction with Deep Neural Networks—0
Stein Point Markov Chain Monte CarloCode0
A Latent Variational Framework for Stochastic Optimization—0
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluationsCode0
Variational Domain Adaptation—0
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout—0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Reversible Jump Probabilistic ProgrammingCode0
A Bayesian Perspective on the Deep Image PriorCode0
Few-Shot Bayesian Imitation Learning with Logical Program Policies—0
Compressed sensing reconstruction using Expectation Propagation—0
A Generalization Bound for Online Variational Inference—0
Bayesian Neural Networks at Finite TemperatureCode0
The Kikuchi Hierarchy and Tensor PCA—0
Generalized Variational Inference: Three arguments for deriving new PosteriorsCode0
Robust Optimisation Monte CarloCode0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints—0
Pairwise Comparisons with Flexible Time-DynamicsCode0
Combining Model and Parameter Uncertainty in Bayesian Neural NetworksCode0
Weighted Mean Curvature—0
Functional Variational Bayesian Neural NetworksCode0
A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior—0
Elements of Sequential Monte Carlo—0
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory—0
Embarrassingly parallel MCMC using deep invertible transformations—0
Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Joint Perception and Control as Inference with an Object-based Implementation—0
Approximation Properties of Variational Bayes for Vector Autoregressions—0
Adaptive Gaussian Copula ABC—0
Bayesian Convolutional Neural Networks for Compressed Sensing RestorationCode0
Gaussian Process Priors for Dynamic Paired Comparison ModellingCode0
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPsCode0
Bayesian Online Prediction of Change PointsCode0
Manifold Optimization Assisted Gaussian Variational Approximation—0
Cyclical Stochastic Gradient MCMC for Bayesian Deep LearningCode1
A stochastic version of Stein Variational Gradient Descent for efficient sampling—0
Low-pass filtering as Bayesian inference—0
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior BootstrapCode0
A Simple Baseline for Bayesian Uncertainty in Deep LearningCode1
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Benchmark Results

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