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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 26–50 of 2226 papers

TitleStatusHype
TimePFN: Effective Multivariate Time Series Forecasting with Synthetic DataCode1
VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR IdentificationCode1
Can Transformers Learn Full Bayesian Inference in Context?Code1
BayesianFitForecast: A User-Friendly R Toolbox for Parameter Estimation and Forecasting with Ordinary Differential EquationsCode1
Scalable Random Feature Latent Variable ModelsCode1
Triple equivalence for the emergence of biological intelligenceCode1
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithmCode1
Persistent Sampling: Enhancing the Efficiency of Sequential Monte CarloCode1
Torchtree: flexible phylogenetic model development and inference using PyTorchCode1
Poisson Variational AutoencoderCode1
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language ModelsCode1
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context LearningCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential EquationsCode1
Bayesian Diffusion Models for 3D Shape ReconstructionCode1
Listening to the Noise: Blind Denoising with Gibbs DiffusionCode1
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse PlanningCode1
Bayesian Uncertainty for Gradient Aggregation in Multi-Task LearningCode1
Diffusive Gibbs SamplingCode1
Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space modelsCode1
Continual Learning via Sequential Function-Space Variational InferenceCode1
Diffusion Models With Learned Adaptive NoiseCode1
Gaussian process learning of nonlinear dynamicsCode1
Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief PropagationCode1
Uncertainty Quantification and Propagation in Surrogate-based Bayesian InferenceCode1
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Benchmark Results

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