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

November 24, 2022

MP-GELU Bayesian Neural Networks: Moment Propagation by GELU Nonlinearity
Yuki Hirayama, Sinya Takamaeda-Yamazaki
Variational Inference Bayesian Neural Network Moment Propagation

November 21, 2022

Bayesian Learning for Neural Networks: an algorithmic survey
Martin Magris, Alexandros Iosifidis
Neural Network Variational Inference Bayesian Neural Network Bayesian Inference Bayesian Learning Algorithmic Task

November 16, 2022

An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Jiayu Yao, Yaniv Yacoby, Beau Coker, Weiwei Pan, Finale Doshi-Velez
Scientific Inference Bayesian Neural Network Empirical Analysis Advantage Feedback Model Mismatch

November 15, 2022

On the Performance of Direct Loss Minimization for Bayesian Neural Networks
Yadi Wei, Roni Khardon
Loss Function System Performance Bayesian Neural Network Loss Minimization

November 14, 2022

Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification
Mingyang Zhang, Ziqi Di, Maoguo Gong, Yue Wu, Hao Li, Xiangming Jiang
Graph Neural Network Generative Adversarial Network Hyperspectral Image Bayesian Neural Network Hyperspectral Image Classification

November 11, 2022

Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, Tom Rainforth
Bayesian Neural Network Stochastic Process Estimation Bias Stochastic Network Universal Prediction

November 9, 2022

Workload Forecasting of a Logistic Node Using Bayesian Neural Networks
Emin Nakilcioglu, Anisa Rizvanolli und Olaf Rendel
Bayesian Neural Network Tractor Trailer Vehicle Workload Forecasting

October 31, 2022

October 23, 2022

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Zhijie Deng, Feng Zhou, Jun Zhu
Bayesian Neural Network Neural Tangent Kernel Bayesian Deep Learning Gauss Newton Laplace Approximation

October 21, 2022

Bayesian deep learning framework for uncertainty quantification in high dimensions
Jeahan Jung, Minseok Choi
Uncertainty Quantification Bayesian Neural Network Bayesian Inference High Dimension Bayesian Deep Learning Hamiltonian Monte Carlo

October 19, 2022

October 18, 2022

An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty
Ethan Ancell, Christopher Bennett, Bert Debusschere, Sapan Agarwal, Park Hays, T. Patrick Xiao
Neural Network Generative Adversarial Network Bayesian Neural Network Epistemic Uncertainty

October 17, 2022

October 16, 2022

Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints
Jiayu Huang, Yutian Pang, Yongming Liu, Hao Yan
Uncertainty Quantification Bayesian Neural Network Posterior Mode Neural Network Parameter Bayesian Regularization

October 12, 2022

Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning
Marek Herde, Zhixin Huang, Denis Huseljic, Daniel Kottke, Stephan Vogt, Bernhard Sick
Deep Learning Active Learning Bayesian Neural Network Use Case Neural Network Gaussian Process Bayesian Update

October 10, 2022

Layer Ensembles
Illia Oleksiienko, Alexandros Iosifidis
Uncertainty Estimation Bayesian Neural Network Deep Ensemble Intrinsic Uncertainty Layer Ensemble

October 3, 2022

Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Yunshi Huang, Emilie Chouzenoux, Victor Elvira, Jean-Christophe Pesquet
Neural Network Adaptive Importance Bayesian Neural Network Bayesian Approach Efficient Bayesian Inference

Bayesian Neural Network

Papers

MP-GELU Bayesian Neural Networks: Moment Propagation by GELU Nonlinearity

Bayesian Learning for Neural Networks: an algorithmic survey

An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks

On the Performance of Direct Loss Minimization for Bayesian Neural Networks

Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification

Do Bayesian Neural Networks Need To Be Fully Stochastic?

Workload Forecasting of a Logistic Node Using Bayesian Neural Networks

Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks

Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

Bayesian deep learning framework for uncertainty quantification in high dimensions

Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing

Variational Model Perturbation for Source-Free Domain Adaptation

An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty

Data Subsampling for Bayesian Neural Networks

Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization

Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints

Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning

Layer Ensembles

Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling