Sparse Bayesian

Sparse Bayesian methods aim to efficiently learn models with a small number of significant parameters from often noisy data, improving both computational efficiency and model interpretability. Current research focuses on applying these techniques within various frameworks, including Bayesian neural networks, variational inference, and dictionary learning, often incorporating advanced algorithms like approximate message passing and Bayesian optimization to enhance performance and robustness. These advancements are impacting diverse fields, enabling more reliable fault detection in industrial processes, improved brain-computer interfaces through more accurate neural decoding, and more efficient modeling of complex dynamical systems. The resulting models offer not only improved prediction accuracy but also valuable uncertainty quantification.

Papers

August 7, 2024

Maximum a Posteriori Estimation for Linear Structural Dynamics Models Using Bayesian Optimization with Rational Polynomial Chaos Expansions
Felix Schneider, Iason Papaioannou, Bruno Sudret, Gerhard Müller
Bayesian Optimization Bayesian Perspective Structural Dynamic Polynomial Chaos Expansion Maximum Mean Posteriori Error Sparse Bayesian Learning Sparse Bayesian

April 15, 2024

Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
Qi Zhang, Lei Xie, Weihua Xu, Hongye Su
Medical Diagnosis Dictionary Learning Fuel Cell Sparse Bayesian Modular Electrolysis Plant

April 1, 2024

Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings
Yuanhao Li, Badong Chen, Natsue Yoshimura, Yasuharu Koike, Okito Yamashita
Neural Recording Sparse Bayesian Learning Sparse Bayesian Correntropy Learning

February 16, 2024

Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Junbo Li, Zichen Miao, Qiang Qiu, Ruqi Zhang
Bayesian Neural Network Sparse Variational Sparse Subspace Sparse Bayesian

February 12, 2024

Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing
Wei Xu, An Liu, Yiting Zhang, Vincent Lau
Model Compression Bayesian Deep Learning Approximate Message Bayesian Deep Global Posterior Sparse Bayesian

October 10, 2023

A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data
Tapas Tripura, Souvik Chakraborty
Raw Data Dynamical System Physical System Lagrangian System Sparse Bayesian

March 14, 2023

Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks
Arunesh Mittal, Kai Yang, Paul Sajda, John Paisley
Deep Neural Network Deep Model Latent Variable Model Approximate Inference Discrete Latent Representation Amortised Inference Sparse Bayesian

October 7, 2022

Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
Variational Inference Neural ODE Sparse Bayesian

Sparse Bayesian

Papers

Maximum a Posteriori Estimation for Linear Structural Dynamics Models Using Bayesian Optimization with Rational Polynomial Chaos Expansions

Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning

Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings

Training Bayesian Neural Networks with Sparse Subspace Variational Inference

Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing

A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data

Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks

Latent Neural ODEs with Sparse Bayesian Multiple Shooting