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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1651–1700 of 2226 papers

TitleStatusHype
Functional Variational Bayesian Neural NetworksCode0
A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior—0
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory—0
Elements of Sequential Monte Carlo—0
Embarrassingly parallel MCMC using deep invertible transformations—0
Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Joint Perception and Control as Inference with an Object-based Implementation—0
Approximation Properties of Variational Bayes for Vector Autoregressions—0
Adaptive Gaussian Copula ABC—0
Bayesian Convolutional Neural Networks for Compressed Sensing RestorationCode0
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPsCode0
Gaussian Process Priors for Dynamic Paired Comparison ModellingCode0
Bayesian Online Prediction of Change PointsCode0
Manifold Optimization Assisted Gaussian Variational Approximation—0
Low-pass filtering as Bayesian inference—0
A stochastic version of Stein Variational Gradient Descent for efficient sampling—0
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior BootstrapCode0
A Bayesian Approach for Accurate Classification-Based Aggregates—0
Stochastic Zeroth-order Discretizations of Langevin Diffusions for Bayesian Inference—0
Predictive Uncertainty Quantification with Compound Density Networks—0
Sequential Bayesian Detection of Spike Activities from Fluorescence Observations—0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Metric Gaussian Variational InferenceCode0
Stochastic Gradient MCMC for Nonlinear State Space ModelsCode0
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large DatasetsCode0
What does the free energy principle tell us about the brain?—0
Fitting A Mixture Distribution to Data: TutorialCode0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
Mixed Variational InferenceCode0
Multi-modal Ensemble Classification for Generalized Zero Shot Learning—0
Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership—0
Undirected Graphical Models as Approximate PosteriorsCode0
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational InferenceCode0
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning—0
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning—0
Guess Who's Coming (and Who's Going): Bringing Perspective to the Rational Speech Acts Framework—0
Accelerated MM Algorithms for Ranking Scores Inference from Comparison DataCode0
Probabilistic Streaming Tensor DecompositionCode0
Meta Architecture SearchCode0
Surrogate-assisted Bayesian inversion for landscape and basin evolution modelsCode0
Probabilistic Model Checking of Robots Deployed in Extreme Environments—0
Physics-Based Learning for Robotic Environmental Sensing—0
Sampling-based Bayesian Inference with gradient uncertainty—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
That's Mine! Learning Ownership Relations and Norms for Robots—0
Predictive Approximate Bayesian Computation via Saddle Points—0
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues—0
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs—0
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Benchmark Results

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