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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 1201–1225 of 2226 papers

TitleStatusHype
Metropolis-CVAE: Bootstrapping Labels for Bayesian Inference via Semi-Supervised Conditional Variational Autoencoders—0
Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Sampling with Trusthworthy Constraints: A Variational Gradient FrameworkCode0
Improved Neuronal Ensemble Inference with Generative Model and MCMC—0
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
Parametrization invariant interpretation of priors and posteriors—0
Bayesian reconstruction of memories stored in neural networks from their connectivityCode0
Attentional Prototype Inference for Few-Shot SegmentationCode0
Priors in Bayesian Deep Learning: A Review—0
High-Performance FPGA-based Accelerator for Bayesian Neural Networks—0
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Bayesian inference and superstatistics to describe long memory processes of financial time series—0
Laplace Matching for fast Approximate Inference in Latent Gaussian ModelsCode0
MCMC-driven importance samplers—0
Bayesian Logistic Shape Model Inference: application to cochlea image segmentation—0
Consumer Demand Modeling During COVID-19 Pandemic—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Invariant polynomials and machine learningCode0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Bayesian Numerical Methods for Nonlinear Partial Differential Equations—0
Robust Generalised Bayesian Inference for Intractable LikelihoodsCode0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inferenceCode0
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Benchmark Results

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