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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 76–100 of 2226 papers

TitleStatusHype
BayesianFitForecast: A User-Friendly R Toolbox for Parameter Estimation and Forecasting with Ordinary Differential EquationsCode1
Bayesian graph convolutional neural networks via tempered MCMCCode1
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
A Simple Baseline for Bayesian Uncertainty in Deep LearningCode1
Bayesian Uncertainty for Gradient Aggregation in Multi-Task LearningCode1
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU NetworksCode1
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context LearningCode1
A Probabilistic State Space Model for Joint Inference from Differential Equations and DataCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
ComBiNet: Compact Convolutional Bayesian Neural Network for Image SegmentationCode1
Complete parameter inference for GW150914 using deep learningCode1
Convolutional Bayesian Kernel Inference for 3D Semantic MappingCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
Cyclical Stochastic Gradient MCMC for Bayesian Deep LearningCode1
D3p -- A Python Package for Differentially-Private Probabilistic ProgrammingCode1
Accelerated Bayesian SED Modeling using Amortized Neural Posterior EstimationCode1
Deep Bayesian Unsupervised Lifelong LearningCode1
Understanding and Accelerating Particle-Based Variational InferenceCode1
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
Distilled Self-Critique of LLMs with Synthetic Data: a Bayesian PerspectiveCode1
A practical tutorial on Variational BayesCode1
Domain Adaptation as a Problem of Inference on Graphical ModelsCode1
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningCode1
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variableCode1
A Primer on Bayesian Neural Networks: Review and DebatesCode1
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Benchmark Results

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