Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Jimenez, Felix, Katzfuss, Matthias |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
by: Jimenez, Felix, et al.
Published: (2023)
by: Jimenez, Felix, et al.
Published: (2023)
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)
Probabilistic Consistency in Machine Learning and Its Connection to Uncertainty Quantification
by: Patrone, Paul, et al.
Published: (2025)
by: Patrone, Paul, et al.
Published: (2025)
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)
Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage
by: Chakraborty, Anirban, et al.
Published: (2024)
by: Chakraborty, Anirban, et al.
Published: (2024)
Computing Linear Regions in Neural Networks with Skip Connections
by: Joyce, Johnny, et al.
Published: (2025)
by: Joyce, Johnny, et al.
Published: (2025)
Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturing
by: Sanpui, Dipayan, et al.
Published: (2025)
by: Sanpui, Dipayan, et al.
Published: (2025)
Fast Gaussian Process Approximations for Autocorrelated Data
by: Chokhachian, Ahmadreza, et al.
Published: (2025)
by: Chokhachian, Ahmadreza, et al.
Published: (2025)
Probabilistic Classification and Uncertainty Quantification of Sahara Desert Climate Using Feedforward Neural Networks
by: Tivenan, Stephen, et al.
Published: (2026)
by: Tivenan, Stephen, et al.
Published: (2026)
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
by: Zhang, Jiaxin, et al.
Published: (2024)
by: Zhang, Jiaxin, et al.
Published: (2024)
Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells
by: Kittelsen, Jonas Ekeland, et al.
Published: (2024)
by: Kittelsen, Jonas Ekeland, et al.
Published: (2024)
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
by: Monod, Mélodie, et al.
Published: (2025)
by: Monod, Mélodie, et al.
Published: (2025)
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)
The SkipSponge Attack: Sponge Weight Poisoning of Deep Neural Networks
by: Lintelo, Jona te, et al.
Published: (2024)
by: Lintelo, Jona te, et al.
Published: (2024)
Zero-Shot Uncertainty Quantification using Diffusion Probabilistic Models
by: Shu, Dule, et al.
Published: (2024)
by: Shu, Dule, 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)
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
by: Wu, Luhuan, et al.
Published: (2024)
by: Wu, Luhuan, et al.
Published: (2024)
Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey
by: Xu, Guoping, et al.
Published: (2024)
by: Xu, Guoping, et al.
Published: (2024)
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)
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)
Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks
by: Jafari, Akbar Anbar, et al.
Published: (2026)
by: Jafari, Akbar Anbar, 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)
On the Adversarial Transferability of Generalized "Skip Connections"
by: Wang, Yisen, et al.
Published: (2024)
by: Wang, Yisen, et al.
Published: (2024)
Uncertainty Quantification Metrics for Deep Regression
by: Lind, Simon Kristoffersson, et al.
Published: (2024)
by: Lind, Simon Kristoffersson, et al.
Published: (2024)
Improving Deep Learning Model Calibration for Cardiac Applications using Deterministic Uncertainty Networks and Uncertainty-aware Training
by: Dawood, Tareen, et al.
Published: (2024)
by: Dawood, Tareen, et al.
Published: (2024)
The Probabilistic Tsetlin Machine: A Novel Approach to Uncertainty Quantification
by: Abeyrathna, K. Darshana, et al.
Published: (2024)
by: Abeyrathna, K. Darshana, et al.
Published: (2024)
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)
Can an MLP Absorb Its Own Skip Connection?
by: Mijoski, Antonij, et al.
Published: (2026)
by: Mijoski, Antonij, et al.
Published: (2026)
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
by: Vadillo, Jon, et al.
Published: (2024)
by: Vadillo, Jon, et al.
Published: (2024)
Probabilistic Super-Resolution for High-Fidelity Physical System Simulations with Uncertainty Quantification
by: Zhang, Pengyu, et al.
Published: (2025)
by: Zhang, Pengyu, et al.
Published: (2025)
Uncertainty Quantification with the Empirical Neural Tangent Kernel
by: Wilson, Joseph, et al.
Published: (2025)
by: Wilson, Joseph, 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)
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
by: Valiuddin, M. M. A., et al.
Published: (2024)
by: Valiuddin, M. M. A., et al.
Published: (2024)
Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks
by: Khot, Ayush, et al.
Published: (2025)
by: Khot, Ayush, et al.
Published: (2025)
Skip-Connected Policy Optimization for Implicit Advantage
by: Teng, Fengwei, et al.
Published: (2026)
by: Teng, Fengwei, et al.
Published: (2026)
Similar Items
-
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
by: Jimenez, Felix, et al.
Published: (2023) -
Quantification of Uncertainties in Probabilistic Deep Neural Network by Implementing Boosting of Variational Inference
by: Bera, Pavia, et al.
Published: (2025) -
Probabilistic Consistency in Machine Learning and Its Connection to Uncertainty Quantification
by: Patrone, Paul, et al.
Published: (2025) -
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
by: Wang, Qingyi, et al.
Published: (2023) -
Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage
by: Chakraborty, Anirban, et al.
Published: (2024)