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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 1801–1850 of 2226 papers

TitleStatusHype
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Analysis of Langevin Monte Carlo via convex optimization—0
Conditionally Independent Multiresolution Gaussian ProcessesCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Variational Autoencoders for Collaborative FilteringCode0
Variational Autoencoders for Collaborative FilteringCode1
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization—0
Improving Quadrature for Constrained IntegrandsCode0
Bayesian inference for bivariate ranks—0
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration—0
Weighted Community Detection and Data Clustering Using Message Passing—0
A Review of Multiple Try MCMC algorithms for Signal Processing—0
A Distributed Framework for the Construction of Transport Maps—0
Bayesian reconstruction of HIV transmission trees from viral sequences and uncertain infection times—0
Experimentally detecting a quantum change point via Bayesian inference—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections—0
Gaussian Process Neurons—0
Interpreting Deep Classification Models With Bayesian Inference—0
A Scalable Laplace Approximation for Neural NetworksCode0
On Statistical Optimality of Variational Bayes—0
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators—0
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise—0
Assumed Density Filtering Q-learningCode0
Approximations in the homogeneous Ising model—0
Episodic memory for continual model learning—0
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit—0
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal NeuronsCode0
Molecular Clock Dating using MrBayes—0
Riemannian Stein Variational Gradient Descent for Bayesian InferenceCode0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example—0
A Parameter-Free Learning Automaton Scheme—0
Computing the quality of the Laplace approximation—0
Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data—0
A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles of phylogenies—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Bootstrapped synthetic likelihood—0
Message Passing Stein Variational Gradient Descent—0
Variational Gaussian Dropout is not Bayesian—0
Flexible statistical inference for mechanistic models of neural dynamicsCode0
Spintronics based Stochastic Computing for Efficient Bayesian Inference System—0
Learning Asymmetric and Local Features in Multi-Dimensional Data through Wavelets with Recursive PartitioningCode0
Deep Neural Networks as Gaussian ProcessesCode0
Tensor Regression Meets Gaussian Processes—0
Grammar Induction for Minimalist Grammars using Variational Bayesian Inference : A Technical ReportCode0
Implicit Causal Models for Genome-wide Association Studies—0
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection—0
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference—0
Bayesian Inference over the Stiefel Manifold via the Givens Representation—0
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Benchmark Results

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