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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 126–150 of 2226 papers

TitleStatusHype
VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR IdentificationCode1
False Discovery Rate Control via Frequentist-assisted Horseshoe—0
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation—0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Generalised Bayesian distance-based phylogenetics for the genomics eraCode0
Posterior SBC: Simulation-Based Calibration Checking Conditional on DataCode0
Constrained belief updates explain geometric structures in transformer representations—0
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation—0
Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural NetworksCode0
Eliciting Language Model Behaviors with Investigator Agents—0
Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems—0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon—0
VKFPos: A Learning-Based Monocular Positioning with Variational Bayesian Extended Kalman Filter IntegrationCode0
BARNN: A Bayesian Autoregressive and Recurrent Neural Network—0
Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering—0
Can Transformers Learn Full Bayesian Inference in Context?Code1
Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data—0
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields—0
Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective—0
Can Bayesian Neural Networks Make Confident Predictions?—0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization—0
FLORA: Formal Language Model Enables Robust Training-free Zero-shot Object Referring Analysis—0
A recursive Bayesian neural network for constitutive modeling of sands under monotonic loading—0
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Benchmark Results

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