Uncertainty Quantification for Gradient-based Explanations in Neural Networks
Fuente:
arXiv
Guardado en:
| Autores principales: | Mulye, Mihir, Valdenegro-Toro, Matias |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Sanity Checks for Explanation Uncertainty
por: Valdenegro-Toro, Matias, et al.
Publicado: (2024)
por: Valdenegro-Toro, Matias, et al.
Publicado: (2024)
Can Bayesian Neural Networks Explicitly Model Input Uncertainty?
por: Valdenegro-Toro, Matias, et al.
Publicado: (2025)
por: Valdenegro-Toro, Matias, et al.
Publicado: (2025)
Quantile-Free Uncertainty Quantification in Graph Neural Networks
por: park, Soyoung, et al.
Publicado: (2026)
por: park, Soyoung, et al.
Publicado: (2026)
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
por: Duell, Jamie, et al.
Publicado: (2024)
por: Duell, Jamie, et al.
Publicado: (2024)
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
por: Yu, Dahai, et al.
Publicado: (2025)
por: Yu, Dahai, et al.
Publicado: (2025)
Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
por: Nguyen, Quang-Huy, et al.
Publicado: (2026)
por: Nguyen, Quang-Huy, et al.
Publicado: (2026)
The Dilemma of Uncertainty Estimation for General Purpose AI in the EU AI Act
por: Valdenegro-Toro, Matias, et al.
Publicado: (2024)
por: Valdenegro-Toro, Matias, et al.
Publicado: (2024)
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
por: Prabhushankar, Mohit, et al.
Publicado: (2024)
por: Prabhushankar, Mohit, et al.
Publicado: (2024)
An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
por: Grünefeld, Nils, et al.
Publicado: (2026)
por: Grünefeld, Nils, et al.
Publicado: (2026)
Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks
por: Jafari, Akbar Anbar, et al.
Publicado: (2026)
por: Jafari, Akbar Anbar, et al.
Publicado: (2026)
Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
por: Sokol, Kacper, et al.
Publicado: (2025)
por: Sokol, Kacper, et al.
Publicado: (2025)
Mapping Transformer Leveraged Embeddings for Cross-Lingual Document Representation
por: Tashu, Tsegaye Misikir, et al.
Publicado: (2024)
por: Tashu, Tsegaye Misikir, et al.
Publicado: (2024)
DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling
por: Akhare, Deepak, et al.
Publicado: (2023)
por: Akhare, Deepak, et al.
Publicado: (2023)
Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
por: Song, Haoze, et al.
Publicado: (2026)
por: Song, Haoze, et al.
Publicado: (2026)
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
por: de Jong, Ivo Pascal, et al.
Publicado: (2023)
por: de Jong, Ivo Pascal, et al.
Publicado: (2023)
Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
por: Suurmeijer, Joris, et al.
Publicado: (2025)
por: Suurmeijer, Joris, et al.
Publicado: (2025)
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
por: Zhang, Jiaxing, et al.
Publicado: (2025)
por: Zhang, Jiaxing, et al.
Publicado: (2025)
Score-based Integrated Gradient for Root Cause Explanations of Outliers
por: Nguyen, Phuoc, et al.
Publicado: (2026)
por: Nguyen, Phuoc, et al.
Publicado: (2026)
Uncertainty Quantification in the Tsetlin Machine
por: Helin, Runar, et al.
Publicado: (2025)
por: Helin, Runar, et al.
Publicado: (2025)
Uncertainty Quantification in SVM prediction
por: Anand, Pritam
Publicado: (2025)
por: Anand, Pritam
Publicado: (2025)
Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration
por: Decker, Thomas, et al.
Publicado: (2025)
por: Decker, Thomas, et al.
Publicado: (2025)
Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations
por: Decker, Thomas, et al.
Publicado: (2025)
por: Decker, Thomas, et al.
Publicado: (2025)
Developing Distance-Aware, and Evident Uncertainty Quantification in Dynamic Physics-Constrained Neural Networks for Robust Bearing Degradation Estimation
por: Razzaq, Waleed, et al.
Publicado: (2025)
por: Razzaq, Waleed, et al.
Publicado: (2025)
The GECo algorithm for Graph Neural Networks Explanation
por: Calderaro, Salvatore, et al.
Publicado: (2024)
por: Calderaro, Salvatore, et al.
Publicado: (2024)
Regularizing Explanations in Bayesian Convolutional Neural Networks
por: Bekkemoen, Yanzhe, et al.
Publicado: (2021)
por: Bekkemoen, Yanzhe, et al.
Publicado: (2021)
Pathwise Explanation of ReLU Neural Networks
por: Lim, Seongwoo, et al.
Publicado: (2025)
por: Lim, Seongwoo, et al.
Publicado: (2025)
GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks
por: Sanderson, Jacob, et al.
Publicado: (2026)
por: Sanderson, Jacob, et al.
Publicado: (2026)
Uncertainty Quantification for cross-subject Motor Imagery classification
por: Manivannan, Prithviraj, et al.
Publicado: (2024)
por: Manivannan, Prithviraj, et al.
Publicado: (2024)
Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning
por: Brouwer, Eric, et al.
Publicado: (2024)
por: Brouwer, Eric, et al.
Publicado: (2024)
Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models
por: Groot, Tobias, et al.
Publicado: (2024)
por: Groot, Tobias, et al.
Publicado: (2024)
Credal Ensemble Distillation for Uncertainty Quantification
por: Wang, Kaizheng, et al.
Publicado: (2025)
por: Wang, Kaizheng, et al.
Publicado: (2025)
Fair Uncertainty Quantification for Depression Prediction
por: Li, Yonghong, et al.
Publicado: (2025)
por: Li, Yonghong, et al.
Publicado: (2025)
Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification
por: Lafage, Adrien, et al.
Publicado: (2025)
por: Lafage, Adrien, et al.
Publicado: (2025)
Uncertainty in Semantic Language Modeling with PIXELS
por: Radu, Stefania, et al.
Publicado: (2025)
por: Radu, Stefania, et al.
Publicado: (2025)
Graph Neural Network Causal Explanation via Neural Causal Models
por: Behnam, Arman, et al.
Publicado: (2024)
por: Behnam, Arman, et al.
Publicado: (2024)
Calibrated Explanations: with Uncertainty Information and Counterfactuals
por: Lofstrom, Helena, et al.
Publicado: (2023)
por: Lofstrom, Helena, et al.
Publicado: (2023)
Game-theoretic Counterfactual Explanation for Graph Neural Networks
por: Chhablani, Chirag, et al.
Publicado: (2024)
por: Chhablani, Chirag, et al.
Publicado: (2024)
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
por: Flores, Pablo, et al.
Publicado: (2025)
por: Flores, Pablo, et al.
Publicado: (2025)
FAME: Formal Abstract Minimal Explanation for Neural Networks
por: Boumazouza, Ryma, et al.
Publicado: (2026)
por: Boumazouza, Ryma, et al.
Publicado: (2026)
How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
por: Ma, Bin, et al.
Publicado: (2025)
por: Ma, Bin, et al.
Publicado: (2025)
Ejemplares similares
-
Sanity Checks for Explanation Uncertainty
por: Valdenegro-Toro, Matias, et al.
Publicado: (2024) -
Can Bayesian Neural Networks Explicitly Model Input Uncertainty?
por: Valdenegro-Toro, Matias, et al.
Publicado: (2025) -
Quantile-Free Uncertainty Quantification in Graph Neural Networks
por: park, Soyoung, et al.
Publicado: (2026) -
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
por: Duell, Jamie, et al.
Publicado: (2024) -
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
por: Yu, Dahai, et al.
Publicado: (2025)