SOTAVerified

Bayesian Inference

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

Papers

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Showing 1126–1150 of 2226 papers

TitleStatusHype
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
A Bit More Bayesian: Domain-Invariant Learning with UncertaintyCode1
Laplace Matching for fast Approximate Inference in Latent Gaussian ModelsCode0
MCMC-driven importance samplers—0
Bayesian Logistic Shape Model Inference: application to cochlea image segmentation—0
Consumer Demand Modeling During COVID-19 Pandemic—0
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlasCode1
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical ImagingCode1
Invariant polynomials and machine learningCode0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Bayesian Numerical Methods for Nonlinear Partial Differential Equations—0
Bayesian graph convolutional neural networks via tempered MCMCCode1
Robust Generalised Bayesian Inference for Intractable LikelihoodsCode0
Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inferenceCode0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
ComBiNet: Compact Convolutional Bayesian Neural Network for Image SegmentationCode1
The computational asymptotics of Gaussian variational inference and the Laplace approximationCode0
Learning by example: fast reliability-aware seismic imaging with normalizing flowsCode1
Revisiting Bayesian Autoencoders with MCMCCode0
COVID-19 detection using chest X-rays: is lung segmentation important for generalization?Code0
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC—0
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation—0
A Bayesian Approach to Identifying Representational Errors—0
W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality—0
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Benchmark Results

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