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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 1501–1550 of 2226 papers

TitleStatusHype
Stein Variational Gradient Descent With Matrix-Valued KernelsCode0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric UncertaintyCode0
Variational Predictive Information Bottleneck—0
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models—0
Safe-Bayesian Generalized Linear Regression—0
Why bigger is not always better: on finite and infinite neural networks—0
Efficient Bayesian Inference for Nested Simulators—0
AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithmsCode0
Variationally Inferred Sampling Through a Refined BoundCode0
Stein Variational Gradient Descent for Approximate Bayesian Computation—0
Bijectors.jl: Flexible transformations for probability distributionsCode0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Exchangeable Variational Autoencoders with Applications to Genomic Data—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors—0
Neural Permutation Processes—0
Challenges in Markov chain Monte Carlo for Bayesian neural networksCode0
Variational Tracking and Prediction with Generative Disentangled State-Space Models—0
Bayesian Integration of Multi-resolutional Grid Codes for Spatial Cognition—0
Distilling Importance Sampling for Likelihood Free InferenceCode0
Stochastic triangular mesh mapping: A terrain mapping technique for autonomous mobile robots—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization—0
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models—0
Inference of a mesoscopic population model from population spike trainsCode0
The Neural Moving Average Model for Scalable Variational Inference of State Space ModelsCode0
Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition—0
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference—0
Non-Gaussian processes and neural networks at finite widths—0
Debiased Bayesian inference for average treatment effectsCode0
Refining the variational posterior through iterative optimization—0
Gaussian Process Meta-Representations Of Neural Networks—0
On the Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks—0
Meta-Learning for Variational Inference—0
Deep Bayesian Structure NetworksCode0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex—0
Learning Curves for Deep Neural Networks: A field theory perspective—0
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)—0
Mixture Distributions for Scalable Bayesian Inference—0
Quantifying uncertainty with GAN-based priors—0
A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series—0
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control—0
Learn to Estimate Labels Uncertainty for Quality Assurance—0
Compositional uncertainty in deep Gaussian processesCode0
Bayesian inference of species trees using diffusion modelsCode0
Prediction Uncertainty Estimation for Hate Speech Classification—0
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Benchmark Results

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