Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective
Fuente:
arXiv
Saved in:
| Main Authors: | Yuan, Xueqiong, Li, Jipeng, Kuruoglu, Ercan Engin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bayesian Neural Networks: A Min-Max Game Framework
by: Hong, Junping, et al.
Published: (2023)
by: Hong, Junping, et al.
Published: (2023)
Bayesian Neural Network For Personalized Federated Learning Parameter Selection
by: Luo, Mengen, et al.
Published: (2024)
by: Luo, Mengen, et al.
Published: (2024)
LLM Online Spatial-temporal Signal Reconstruction Under Noise
by: Yan, Yi, et al.
Published: (2024)
by: Yan, Yi, et al.
Published: (2024)
Adaptive Least Mean Squares Graph Neural Networks and Online Graph Signal Estimation
by: Yan, Yi, et al.
Published: (2024)
by: Yan, Yi, et al.
Published: (2024)
Function-Space Empirical Bayes Regularisation with Student's t Priors
by: Hao, Pengcheng, et al.
Published: (2026)
by: Hao, Pengcheng, et al.
Published: (2026)
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding
by: Piriyasatit, Suchanuch, et al.
Published: (2025)
by: Piriyasatit, Suchanuch, et al.
Published: (2025)
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding
by: Piriyasatit, Suchanuch, et al.
Published: (2025)
by: Piriyasatit, Suchanuch, et al.
Published: (2025)
Adaptive Spatio-temporal Estimation on the Graph Edges via Line Graph Transformation
by: Yan, Yi, et al.
Published: (2023)
by: Yan, Yi, et al.
Published: (2023)
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
by: Luo, Mengen, et al.
Published: (2024)
by: Luo, Mengen, et al.
Published: (2024)
On Sequential Maximum a Posteriori Inference for Continual Learning
by: Zhu, Menghao Waiyan William, et al.
Published: (2024)
by: Zhu, Menghao Waiyan William, et al.
Published: (2024)
Binarized Simplicial Convolutional Neural Networks
by: Yan, Yi, et al.
Published: (2024)
by: Yan, Yi, et al.
Published: (2024)
Spatio-Temporal Graph Structure Learning for Earthquake Detection
by: Piriyasatit, Suchanun, et al.
Published: (2025)
by: Piriyasatit, Suchanun, et al.
Published: (2025)
Monte Carlo Functional Regularisation for Continual Learning
by: Hao, Pengcheng, et al.
Published: (2025)
by: Hao, Pengcheng, et al.
Published: (2025)
Sequential Function-Space Variational Inference via Gaussian Mixture Approximation
by: Zhu, Menghao Waiyan William, et al.
Published: (2025)
by: Zhu, Menghao Waiyan William, et al.
Published: (2025)
Adaptive Least Mean pth Power Graph Neural Networks
by: Yan, Yi, et al.
Published: (2024)
by: Yan, Yi, et al.
Published: (2024)
Function-Space Empirical Bayes Regularisation with Large Vision-Language Model Priors
by: Hao, Pengcheng, et al.
Published: (2026)
by: Hao, Pengcheng, et al.
Published: (2026)
Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning
by: Zhang, Yihan, et al.
Published: (2026)
by: Zhang, Yihan, et al.
Published: (2026)
ParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
by: Yuan, Chaohao, et al.
Published: (2025)
by: Yuan, Chaohao, et al.
Published: (2025)
Graph Frequency Features of Cancer Gene Co-Expression Networks
by: Adel, Radwa, et al.
Published: (2023)
by: Adel, Radwa, et al.
Published: (2023)
Sequential Monte Carlo Graph Convolutional Network for Dynamic Brain Connectivity
by: Zhao, Fengfan, et al.
Published: (2023)
by: Zhao, Fengfan, et al.
Published: (2023)
A Survey of Graph Transformers: Architectures, Theories and Applications
by: Yuan, Chaohao, et al.
Published: (2025)
by: Yuan, Chaohao, et al.
Published: (2025)
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
by: Monod, Mélodie, et al.
Published: (2025)
by: Monod, Mélodie, et al.
Published: (2025)
SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction
by: Liu, Hanbing, et al.
Published: (2025)
by: Liu, Hanbing, et al.
Published: (2025)
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
by: Li, Longze, et al.
Published: (2023)
by: Li, Longze, et al.
Published: (2023)
Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models
by: Li, Jipeng, et al.
Published: (2025)
by: Li, Jipeng, et al.
Published: (2025)
Position: There Is No Free Bayesian Uncertainty Quantification
by: Melev, Ivan, et al.
Published: (2025)
by: Melev, Ivan, et al.
Published: (2025)
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks
by: Sheinkman, Alisa, et al.
Published: (2025)
by: Sheinkman, Alisa, et al.
Published: (2025)
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data
by: Xie, Ziyu, et al.
Published: (2023)
by: Xie, Ziyu, et al.
Published: (2023)
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads
by: Vaiuso, Andrea, et al.
Published: (2024)
by: Vaiuso, Andrea, et al.
Published: (2024)
Schrodinger Neural Network and Uncertainty Quantification: Quantum Machine
by: Hammad, M. M.
Published: (2025)
by: Hammad, M. M.
Published: (2025)
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks
by: Wu, Yujia, et al.
Published: (2024)
by: Wu, Yujia, et al.
Published: (2024)
BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classification
by: Hou, Jiacheng, et al.
Published: (2025)
by: Hou, Jiacheng, et al.
Published: (2025)
Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks
by: Du, Hongfei, et al.
Published: (2026)
by: Du, Hongfei, et al.
Published: (2026)
Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
by: Gonzalez, David, et al.
Published: (2026)
by: Gonzalez, David, et al.
Published: (2026)
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
by: Wu, Luhuan, et al.
Published: (2024)
by: Wu, Luhuan, et al.
Published: (2024)
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles
by: Vinchurkar, Tirtha, et al.
Published: (2025)
by: Vinchurkar, Tirtha, et al.
Published: (2025)
Quantile-Free Uncertainty Quantification in Graph Neural Networks
by: park, Soyoung, et al.
Published: (2026)
by: park, Soyoung, et al.
Published: (2026)
Uncertainty Quantification for Gradient-based Explanations in Neural Networks
by: Mulye, Mihir, et al.
Published: (2024)
by: Mulye, Mihir, et al.
Published: (2024)
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
by: Reiser, Philipp, et al.
Published: (2023)
by: Reiser, Philipp, et al.
Published: (2023)
Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference
by: Shih, Frank, et al.
Published: (2025)
by: Shih, Frank, et al.
Published: (2025)
Similar Items
-
Bayesian Neural Networks: A Min-Max Game Framework
by: Hong, Junping, et al.
Published: (2023) -
Bayesian Neural Network For Personalized Federated Learning Parameter Selection
by: Luo, Mengen, et al.
Published: (2024) -
LLM Online Spatial-temporal Signal Reconstruction Under Noise
by: Yan, Yi, et al.
Published: (2024) -
Adaptive Least Mean Squares Graph Neural Networks and Online Graph Signal Estimation
by: Yan, Yi, et al.
Published: (2024) -
Function-Space Empirical Bayes Regularisation with Student's t Priors
by: Hao, Pengcheng, et al.
Published: (2026)