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 1101–1125 of 2226 papers

TitleStatusHype
Locally Differentially Private Bayesian Inference—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
Efficient Gradient Flows in Sliced-Wasserstein SpaceCode0
Bayesian Inference in Physics-Based Nonlinear Flame Models—0
Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs—0
An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Godot is not coming: when we will let innovations enter psychiatry?—0
A Bayesian Approach for Medical Inquiry and Disease Inference in Automated Differential DiagnosisCode0
Probing as Quantifying Inductive BiasCode0
On out-of-distribution detection with Bayesian neural networksCode0
Deep Bayesian inference for seismic imaging with tasksCode0
Kernel Interpolation as a Bayes Point MachineCode0
Pathologies in priors and inference for Bayesian transformers—0
De-randomizing MCMC dynamics with the diffusion Stein operator—0
MetaCOG: A Hierarchical Probabilistic Model for Learning Meta-Cognitive Visual RepresentationsCode0
Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learning—0
Kalman Bayesian Neural Networks for Closed-form Online Learning—0
Arbitrary Marginal Neural Ratio Estimation for Simulation-based InferenceCode0
Semantic Classification and Learning Using a Linear Tranformation Model in a Probabilistic Type Theory with Records—0
On the Implicit Biases of Architecture & Gradient Descent—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Simulation-based Bayesian inference for multi-fingered robotic grasping—0
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—0
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Benchmark Results

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