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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 51–100 of 2226 papers

TitleStatusHype
A Bayesian algorithm for retrosynthesisCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
Bayesian Deep Learning via Subnetwork InferenceCode1
A friendly introduction to triangular transportCode1
FedBE: Making Bayesian Model Ensemble Applicable to Federated LearningCode1
BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy imagesCode1
ForecastPFN: Synthetically-Trained Zero-Shot ForecastingCode1
A Bit More Bayesian: Domain-Invariant Learning with UncertaintyCode1
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
GAN-based Priors for Quantifying UncertaintyCode1
GATSBI: Generative Adversarial Training for Simulation-Based InferenceCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
Continual Learning via Sequential Function-Space Variational InferenceCode1
Neural Clustering ProcessesCode1
Bayesian inference for logistic models using Polya-Gamma latent variablesCode1
Bayesian Diffusion Models for 3D Shape ReconstructionCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
Bayesian Adversarial Human Motion SynthesisCode1
Bayesian Coresets: Revisiting the Nonconvex Optimization PerspectiveCode1
BayesDLL: Bayesian Deep Learning LibraryCode1
BayesFlow: Learning complex stochastic models with invertible neural networksCode1
Bayesian differential programming for robust systems identification under uncertaintyCode1
Bayesian continual learning and forgetting in neural networksCode1
Bayesian Inference with Latent Hamiltonian Neural NetworksCode1
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