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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 1626–1650 of 2226 papers

TitleStatusHype
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-RiskCode0
Exploring helical dynamos with machine learningCode0
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
A Bayesian Perspective on the Deep Image PriorCode0
Reversible Jump Probabilistic ProgrammingCode0
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
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Benchmark Results

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