Bayesian Neural Network

Bayesian neural networks (BNNs) aim to combine the power of deep learning with probabilistic reasoning, enabling models to not only make predictions but also quantify their uncertainty. Current research focuses on improving the efficiency and accuracy of BNN inference, exploring various architectures (like U-Nets and Transformers) and algorithms (including variational inference, MCMC, and ensemble methods) to address challenges like high dimensionality and multimodality in the posterior distribution. This work is significant because reliable uncertainty quantification is crucial for trustworthy AI in safety-critical applications such as medical diagnosis, autonomous driving, and scientific modeling, leading to more robust and explainable AI systems.

Papers

August 17, 2024

Learning to Explore for Stochastic Gradient MCMC
SeungHyun Kim, Seohyeon Jung, Seonghyeon Kim, Juho Lee
LeArning Abstract Multi Modal Bayesian Neural Network Posterior Inference Target Modality

August 10, 2024

Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks
Yoav Gelberg, Tycho F. A. van der Ouderaa, Mark van der Wilk, Yarin Gal
Variational Inference Bayesian Neural Network Weight Space Symmetric Model Variational Inference Problem Invariant Posterior

July 30, 2024

Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo, Arash Mohammadi, Hamid Alinejad-Rokny, Dino Sejdinovic, Damith C. Ranasinghe, Ehsan Abbasnejad
LeArning Abstract Bayesian Neural Network Deep Ensemble Bayesian Perspective Bayesian Learning Practical Approach

July 23, 2024

Deep Bayesian segmentation for colon polyps: Well-calibrated predictions in medical imaging
Daniela L. Ramos, Hector J. Hortua
Semantic Segmentation Bayesian Neural Network Medical Imaging Colorectal Cancer Polyp Calibrated Prediction Deep Bayesian Segmentation Colorectal Polyp

July 8, 2024

Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads
Andrea Vaiuso, Gabriele Immordino, Marcello Righi, Andrea Da Ronch
Uncertainty Quantification Bayesian Neural Network Multi Fidelity Fidelity Model Transonic Buffet

July 7, 2024

Accelerating MRI Uncertainty Estimation with Mask-based Bayesian Neural Network
Zehuan Zhang, Matej Genci, Hongxiang Fan, Andreas Wetscherek, Wayne Luk
Bayesian Neural Network Magnetic Resonance FPGA Accelerator

July 3, 2024

Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Cuong Pham, Cuong C. Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do
Full Model Bayesian Neural Network Mutual Learning Feature Diversity Deterministic Neural Network

July 2, 2024

The Epistemic Uncertainty Hole: an issue of Bayesian Neural Networks
Mohammed Fellaji, Frédéric Pennerath
Bayesian Neural Network Epistemic Uncertainty Bayesian Deep Learning Proper Issue Labeling Epistemic Uncertainty Quantification

July 1, 2024

Bayesian Entropy Neural Networks for Physics-Aware Prediction
Rahul Rathnakumar, Jiayu Huang, Hao Yan, Yongming Liu
Deep Learning Model Uncertainty Quantification Bayesian Neural Network Maximum Entropy Physic Aware

June 21, 2024

June 20, 2024

June 18, 2024

Towards Bayesian Data Selection
Julian Rodemann
Active Learning Semi Supervised Learning Bayesian Neural Network Additive Model Bayes Optimal Bayesian Model Selection

June 16, 2024

Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework
Shuwen Zheng, Zhou Fang, Liang Zhao
Uncertainty Quantification Bayesian Neural Network Travel Mode BNN Layer Uncertainty Based Active Learning Mode Choice

June 11, 2024

Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis
Zeinab Abboud, Herve Lombaert, Samuel Kadoury
Uncertainty Quantification Medical Image Analysis Bayesian Neural Network Bayesian Perspective Predictive Uncertainty Posterior Approximation Bayesian Neural

June 10, 2024

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference
Arnaud Descours, Tom Huix, Arnaud Guillin, Manon Michel, Éric Moulines, Boris Nectoux
Variational Inference Back Propagation Bayesian Neural Network Two Layer Neural Network Central Limit Theorem

June 8, 2024

Bayesian vs. PAC-Bayesian Deep Neural Network Ensembles
Nick Hauptvogel, Christian Igel
Bayesian Neural Network Deep Ensemble Bayesian Perspective PAC Bayesian Bayesian Ensemble

June 6, 2024

Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks
Tristan Cinquin, Robert Bamler
Variational Inference Bayesian Neural Network KL Divergence Informative Prior Posterior Uncertainty Function Space Variational Inference

June 5, 2024

Reparameterization invariance in approximate Bayesian inference
Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel, Søren Hauberg
Bayesian Neural Network Posterior Approximation Reparameterization Method Approximate Bayesian Inference Approximate Posterior

Bayesian Neural Network

Papers

Learning to Explore for Stochastic Gradient MCMC

Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks

Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks

Deep Bayesian segmentation for colon polyps: Well-calibrated predictions in medical imaging

Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads

Accelerating MRI Uncertainty Estimation with Mask-based Bayesian Neural Network

Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning

The Epistemic Uncertainty Hole: an issue of Bayesian Neural Networks

Bayesian Entropy Neural Networks for Physics-Aware Prediction

Flat Posterior Does Matter For Bayesian Model Averaging

Bayesian neural networks for predicting uncertainty in full-field material response

Graph Structure Learning with Interpretable Bayesian Neural Networks

Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA

Towards Bayesian Data Selection

Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework

Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference

Bayesian vs. PAC-Bayesian Deep Neural Network Ensembles

Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks

Reparameterization invariance in approximate Bayesian inference