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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 1076–1100 of 2226 papers

TitleStatusHype
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
De-randomizing MCMC dynamics with the diffusion Stein operator—0
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation—0
Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition—0
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination—0
Density Estimation via Bayesian Inference Engines—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Bayesian geoacoustic inversion using mixture density network—0
Deep Stable neural networks: large-width asymptotics and convergence rates—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Bayesian Flow Networks in Continual Learning—0
A Neural Implementation of the Kalman Filter—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models—0
A Bayesian Inference Framework for Procedural Material Parameter Estimation—0
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning—0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods—0
Deep Maxout Network Gaussian Process—0
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Benchmark Results

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