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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 2001–2050 of 2226 papers

TitleStatusHype
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian ProcessesCode0
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methodsCode0
Functional Ensemble DistillationCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Robustness Guarantees for Bayesian Inference with Gaussian ProcessesCode0
Stochastic Gradient MCMC with Repulsive ForcesCode0
Functional Variational Bayesian Neural NetworksCode0
Robust One Round Federated Learning with Predictive Space Bayesian InferenceCode0
Bayesian Inference on Brain-Computer Interfaces via GLASSCode0
Robust Optimisation Monte CarloCode0
Neural Tangents: Fast and Easy Infinite Neural Networks in PythonCode0
Bayesian inference of species trees using diffusion modelsCode0
Fuzzy Bayesian LearningCode0
Bayesian task embedding for few-shot Bayesian optimizationCode0
Attentional Prototype Inference for Few-Shot SegmentationCode0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Robust probabilistic inference via a constrained transport metricCode0
Neuron's Eye View: Inferring Features of Complex Stimuli from Neural ResponsesCode0
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse ProblemsCode0
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphsCode0
Bayesian score calibration for approximate modelsCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Bayesian Robust Aggregation for Federated LearningCode0
Rule-based Bayesian regressionCode0
Rule-based Evolutionary Bayesian LearningCode0
A digital twin framework for civil engineering structuresCode0
Greedy inference with structure-exploiting lazy mapsCode0
Gaussian Process Priors for Dynamic Paired Comparison ModellingCode0
Bayesian regression and BitcoinCode0
A Theoretically Grounded Application of Dropout in Recurrent Neural NetworksCode0
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural networkCode0
Generalised Bayesian distance-based phylogenetics for the genomics eraCode0
A Bayesian Approach for Medical Inquiry and Disease Inference in Automated Differential DiagnosisCode0
Generalised Bayesian Inference for Discrete Intractable LikelihoodCode0
Asynchronous and Distributed Data Augmentation for Massive Data SettingsCode0
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMsCode0
Stochastic Model of siRNA Endosomal Escape Mediated by Fusogenic Peptides in OVCAR-3Code0
Sampling-based inference for large linear models, with application to linearised LaplaceCode0
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo AlgorithmsCode0
Variational Inference based on Robust DivergencesCode0
Nonparametric Bayesian Deep Networks with Local CompetitionCode0
Generalized Posteriors in Approximate Bayesian ComputationCode0
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-RiskCode0
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive ModellingCode0
Generalized Variational Inference: Three arguments for deriving new PosteriorsCode0
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric UncertaintyCode0
Sampling with Trusthworthy Constraints: A Variational Gradient FrameworkCode0
Bayesian Floor Field: Transferring people flow predictions across environmentsCode0
Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM CommunicationsCode0
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Benchmark Results

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