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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 1151–1200 of 2226 papers

TitleStatusHype
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
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior—0
Understanding and mitigating difficulties in posterior predictive evaluation—0
Understanding Approximation for Bayesian Inference in Neural Networks—0
Understanding Task Representations in Neural Networks via Bayesian Ablation—0
Unified field theoretical approach to deep and recurrent neuronal networks—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift—0
Unrolled denoising networks provably learn optimal Bayesian inference—0
Unsupervised Spectral Learning of WCFG as Low-rank Matrix Completion—0
Unsupervised structured semantic inference for spoken dialog reservation tasks—0
Upper Bound of Real Log Canonical Threshold of Tensor Decomposition and its Application to Bayesian Inference—0
Using Rejuvenation to Improve Particle Filtering for Bayesian Word Segmentation—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging—0
Value of Information and Reward Specification in Active Inference and POMDPs—0
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics—0
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference—0
Variational approximations using Fisher divergence—0
Variational Bayes: A report on approaches and applications—0
Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity—0
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations—0
Variational Bayes for Merging Noisy Databases—0
Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC—0
Variational Bayesian Inference for Crowdsourcing Predictions—0
Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data—0
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Benchmark Results

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