Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures
Fuente:
arXiv
Saved in:
| Main Authors: | Xiong, Ping, Schnake, Thomas, Montavon, Grégoire, Müller, Klaus-Robert, Nakajima, Shinichi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Relevant Walk Search for Explaining Graph Neural Networks
by: Xiong, Ping, et al.
Published: (2026)
by: Xiong, Ping, et al.
Published: (2026)
Efficient Higher-order Subgraph Attribution via Message Passing
by: Xiong, Ping, et al.
Published: (2026)
by: Xiong, Ping, et al.
Published: (2026)
Uncovering the Structure of Explanation Quality with Spectral Analysis
by: Maeß, Johannes, et al.
Published: (2025)
by: Maeß, Johannes, et al.
Published: (2025)
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
by: Schnake, Thomas, et al.
Published: (2024)
by: Schnake, Thomas, et al.
Published: (2024)
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
by: Chormai, Pattarawat, et al.
Published: (2022)
by: Chormai, Pattarawat, et al.
Published: (2022)
Distilling Lightweight Domain Experts from Large ML Models by Identifying Relevant Subspaces
by: Chormai, Pattarawat, et al.
Published: (2026)
by: Chormai, Pattarawat, et al.
Published: (2026)
MambaLRP: Explaining Selective State Space Sequence Models
by: Jafari, Farnoush Rezaei, et al.
Published: (2024)
by: Jafari, Farnoush Rezaei, et al.
Published: (2024)
Investigating the Robustness of Subtask Distillation under Spurious Correlation
by: Chormai, Pattarawat, et al.
Published: (2026)
by: Chormai, Pattarawat, et al.
Published: (2026)
XpertAI: uncovering regression model strategies for sub-manifolds
by: Letzgus, Simon, et al.
Published: (2024)
by: Letzgus, Simon, et al.
Published: (2024)
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
by: Bley, Florian, et al.
Published: (2025)
by: Bley, Florian, et al.
Published: (2025)
Conveyance: A Versatile Framework for Learning in Structured Class Spaces
by: Taha, Yasser, et al.
Published: (2026)
by: Taha, Yasser, et al.
Published: (2026)
Explaining Bayesian Neural Networks
by: Bykov, Kirill, et al.
Published: (2021)
by: Bykov, Kirill, et al.
Published: (2021)
Drainage: A Unifying Framework for Addressing Class Uncertainty
by: Taha, Yasser, et al.
Published: (2025)
by: Taha, Yasser, et al.
Published: (2025)
Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport
by: Naumann, Philip, et al.
Published: (2026)
by: Naumann, Philip, et al.
Published: (2026)
Explaining Predictive Uncertainty by Exposing Second-Order Effects
by: Bley, Florian, et al.
Published: (2024)
by: Bley, Florian, et al.
Published: (2024)
Towards Desiderata-Driven Design of Visual Counterfactual Explainers
by: Bender, Sidney, et al.
Published: (2025)
by: Bender, Sidney, et al.
Published: (2025)
Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions
by: Morik, Marco, et al.
Published: (2024)
by: Morik, Marco, et al.
Published: (2024)
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
by: Esders, Malte, et al.
Published: (2024)
by: Esders, Malte, et al.
Published: (2024)
Self-Supervised Training with Autoencoders for Visual Anomaly Detection
by: Bauer, Alexander, et al.
Published: (2022)
by: Bauer, Alexander, et al.
Published: (2022)
Mitigating Clever Hans Strategies in Image Classifiers through Generating Counterexamples
by: Bender, Sidney, et al.
Published: (2025)
by: Bender, Sidney, et al.
Published: (2025)
The Clever Hans Effect in Unsupervised Learning
by: Kauffmann, Jacob, et al.
Published: (2024)
by: Kauffmann, Jacob, et al.
Published: (2024)
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
by: Baghershahi, Peyman, et al.
Published: (2025)
by: Baghershahi, Peyman, et al.
Published: (2025)
Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena
by: Naumann, Philip, et al.
Published: (2025)
by: Naumann, Philip, et al.
Published: (2025)
Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
by: Eberle, Oliver, et al.
Published: (2023)
by: Eberle, Oliver, et al.
Published: (2023)
Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence
by: Mikriukov, Georgii, et al.
Published: (2026)
by: Mikriukov, Georgii, et al.
Published: (2026)
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
by: Kahouli, Khaled, et al.
Published: (2025)
by: Kahouli, Khaled, et al.
Published: (2025)
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks
by: Schotthöfer, Steffen, et al.
Published: (2025)
by: Schotthöfer, Steffen, et al.
Published: (2025)
Towards Explaining Deep Neural Network Compression Through a Probabilistic Latent Space
by: Mozafari-Nia, Mahsa, et al.
Published: (2024)
by: Mozafari-Nia, Mahsa, et al.
Published: (2024)
Explaining Neural Networks with Reasons
by: Hornischer, Levin, et al.
Published: (2025)
by: Hornischer, Levin, et al.
Published: (2025)
Explaining Graph Neural Networks via Structure-aware Interaction Index
by: Bui, Ngoc, et al.
Published: (2024)
by: Bui, Ngoc, et al.
Published: (2024)
Opportunities and limitations of explaining quantum machine learning
by: Gil-Fuster, Elies, et al.
Published: (2024)
by: Gil-Fuster, Elies, et al.
Published: (2024)
Discrete Latent Structure in Neural Networks
by: Niculae, Vlad, et al.
Published: (2023)
by: Niculae, Vlad, et al.
Published: (2023)
Q-SENN: Quantized Self-Explaining Neural Networks
by: Norrenbrock, Thomas, et al.
Published: (2023)
by: Norrenbrock, Thomas, et al.
Published: (2023)
Labeling Neural Representations with Inverse Recognition
by: Bykov, Kirill, et al.
Published: (2023)
by: Bykov, Kirill, et al.
Published: (2023)
Explaining Deep Neural Networks by Leveraging Intrinsic Methods
by: La Rosa, Biagio
Published: (2024)
by: La Rosa, Biagio
Published: (2024)
Self-Explaining Neural Networks for Business Process Monitoring
by: Bassan, Shahaf, et al.
Published: (2025)
by: Bassan, Shahaf, et al.
Published: (2025)
Molecular relaxation by reverse diffusion with time step prediction
by: Kahouli, Khaled, et al.
Published: (2024)
by: Kahouli, Khaled, et al.
Published: (2024)
A Generalized Unified Skew-Normal Process with Neural Bayes Inference
by: Wang, Kesen, et al.
Published: (2024)
by: Wang, Kesen, et al.
Published: (2024)
Graph-based Integrated Gradients for Explaining Graph Neural Networks
by: Simpson, Lachlan, et al.
Published: (2025)
by: Simpson, Lachlan, et al.
Published: (2025)
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
by: Chen, Zhuomin, et al.
Published: (2024)
by: Chen, Zhuomin, et al.
Published: (2024)
Similar Items
-
Relevant Walk Search for Explaining Graph Neural Networks
by: Xiong, Ping, et al.
Published: (2026) -
Efficient Higher-order Subgraph Attribution via Message Passing
by: Xiong, Ping, et al.
Published: (2026) -
Uncovering the Structure of Explanation Quality with Spectral Analysis
by: Maeß, Johannes, et al.
Published: (2025) -
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
by: Schnake, Thomas, et al.
Published: (2024) -
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
by: Chormai, Pattarawat, et al.
Published: (2022)