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Bayesian Inference

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1376–1400 of 2226 papers

TitleStatusHype
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Transport Gaussian Processes for Regression—0
Transport Quasi-Monte Carlo—0
Tree-Structured Stick Breaking for Hierarchical Data—0
Trust and Time Preference: Measuring a Causal Effect in a Random-Assignment Experiment—0
Tutorial: VAE as an inference paradigm for neuroimaging—0
UAV-Based Remote Sensing of Soil Moisture Across Diverse Land Covers: Validation and Bayesian Uncertainty Characterization—0
Unbiased Bayes for Big Data: Paths of Partial Posteriors—0
Uncertain Evidence in Probabilistic Models and Stochastic Simulators—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference—0
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift—0
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference—0
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models—0
Surrogate Model For Field Optimization Using Beta-VAE Based Regression—0
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning—0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development—0
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning—0
Uncertainty propagation in neural networks for sparse coding—0
Uncertainty quantification in covid-19 spread: lockdown effects—0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective—0
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Benchmark Results

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