Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
Fuente:
arXiv
Saved in:
| Main Authors: | Wu, Luhuan, Williamson, Sinead |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
by: Nagler, Thomas, et al.
Published: (2025)
by: Nagler, Thomas, et al.
Published: (2025)
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks
by: Damke, Clemens, et al.
Published: (2024)
by: Damke, Clemens, et al.
Published: (2024)
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks
by: Wu, Yujia, et al.
Published: (2024)
by: Wu, Yujia, et al.
Published: (2024)
Self-Supervised Learning with Gaussian Processes
by: Duan, Yunshan, et al.
Published: (2025)
by: Duan, Yunshan, et al.
Published: (2025)
Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results
by: Santilli, Andrea, et al.
Published: (2025)
by: Santilli, Andrea, 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)
On the Posterior Distribution in Denoising: Application to Uncertainty Quantification
by: Manor, Hila, et al.
Published: (2023)
by: Manor, Hila, et al.
Published: (2023)
Variational Nearest Neighbor Gaussian Process
by: Wu, Luhuan, et al.
Published: (2022)
by: Wu, Luhuan, et al.
Published: (2022)
Schrodinger Neural Network and Uncertainty Quantification: Quantum Machine
by: Hammad, M. M.
Published: (2025)
by: Hammad, M. M.
Published: (2025)
Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors
by: Beauchamp, Maxime, et al.
Published: (2024)
by: Beauchamp, Maxime, et al.
Published: (2024)
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)
Uncertainty Quantification for Gradient-based Explanations in Neural Networks
by: Mulye, Mihir, et al.
Published: (2024)
by: Mulye, Mihir, et al.
Published: (2024)
Quantile-Free Uncertainty Quantification in Graph Neural Networks
by: park, Soyoung, et al.
Published: (2026)
by: park, Soyoung, et al.
Published: (2026)
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles
by: Vinchurkar, Tirtha, et al.
Published: (2025)
by: Vinchurkar, Tirtha, et al.
Published: (2025)
Uncertainty Quantification Via the Posterior Predictive Variance
by: Chaudhuri, Sanjay, et al.
Published: (2026)
by: Chaudhuri, Sanjay, et al.
Published: (2026)
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)
Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective
by: Yuan, Xueqiong, et al.
Published: (2025)
by: Yuan, Xueqiong, et al.
Published: (2025)
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks
by: Jimenez, Felix, et al.
Published: (2025)
by: Jimenez, Felix, et al.
Published: (2025)
Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks
by: Furlong, Aidan, et al.
Published: (2024)
by: Furlong, Aidan, et al.
Published: (2024)
Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization
by: Li, Minhuan, et al.
Published: (2026)
by: Li, Minhuan, et al.
Published: (2026)
Bayesian Invariance Modeling of Multi-Environment Data
by: Wu, Luhuan, et al.
Published: (2025)
by: Wu, Luhuan, et al.
Published: (2025)
Uncertainty Quantification of Data Shapley via Statistical Inference
by: Wu, Mengmeng, et al.
Published: (2024)
by: Wu, Mengmeng, et al.
Published: (2024)
Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs
by: Valdettaro, Filippo, et al.
Published: (2024)
by: Valdettaro, Filippo, et al.
Published: (2024)
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
by: Wang, Qingyi, et al.
Published: (2023)
by: Wang, Qingyi, et al.
Published: (2023)
Uncertainty Quantification with the Empirical Neural Tangent Kernel
by: Wilson, Joseph, et al.
Published: (2025)
by: Wilson, Joseph, et al.
Published: (2025)
Quantification of Uncertainties in Probabilistic Deep Neural Network by Implementing Boosting of Variational Inference
by: Bera, Pavia, et al.
Published: (2025)
by: Bera, Pavia, et al.
Published: (2025)
Epistemic Uncertainty Quantification For Pre-trained Neural Network
by: Wang, Hanjing, et al.
Published: (2024)
by: Wang, Hanjing, et al.
Published: (2024)
Direct Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Model
by: Faza, Ghifari Adam, et al.
Published: (2026)
by: Faza, Ghifari Adam, et al.
Published: (2026)
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
by: Padmanabha, Govinda Anantha, et al.
Published: (2024)
by: Padmanabha, Govinda Anantha, et al.
Published: (2024)
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
by: Yu, Dahai, et al.
Published: (2025)
by: Yu, Dahai, et al.
Published: (2025)
Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
by: Nguyen, Quang-Huy, et al.
Published: (2026)
by: Nguyen, Quang-Huy, et al.
Published: (2026)
Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks
by: Alsafadi, Farah, et al.
Published: (2024)
by: Alsafadi, Farah, et al.
Published: (2024)
Activation-Space Uncertainty Quantification for Pretrained Networks
by: Bergna, Richard, et al.
Published: (2026)
by: Bergna, Richard, et al.
Published: (2026)
Gibbs Sampling the Posterior of Neural Networks
by: Piccioli, Giovanni, et al.
Published: (2023)
by: Piccioli, Giovanni, et al.
Published: (2023)
Neural Conditional Probability for Uncertainty Quantification
by: Kostic, Vladimir R., et al.
Published: (2024)
by: Kostic, Vladimir R., et al.
Published: (2024)
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
by: Roderick, Melrose, et al.
Published: (2023)
by: Roderick, Melrose, et al.
Published: (2023)
A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
by: Yu, Yifan, et al.
Published: (2025)
by: Yu, Yifan, et al.
Published: (2025)
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification
by: Silva, Vinicius L. S., et al.
Published: (2021)
by: Silva, Vinicius L. S., et al.
Published: (2021)
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
by: Monod, Mélodie, et al.
Published: (2025)
by: Monod, Mélodie, et al.
Published: (2025)
Similar Items
-
Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
by: Nagler, Thomas, et al.
Published: (2025) -
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks
by: Damke, Clemens, et al.
Published: (2024) -
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks
by: Wu, Yujia, et al.
Published: (2024) -
Self-Supervised Learning with Gaussian Processes
by: Duan, Yunshan, et al.
Published: (2025) -
Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results
by: Santilli, Andrea, et al.
Published: (2025)