Saved in:
| Main Authors: | Morala, Pablo, Cifuentes, Jenny Alexandra, Lillo, Rosa E., Ucar, Iñaki |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01588 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
by: Ranasinghe, Nisal, et al.
Published: (2023)
by: Ranasinghe, Nisal, et al.
Published: (2023)
Faithful Interpretation for Graph Neural Networks
by: Hu, Lijie, et al.
Published: (2024)
by: Hu, Lijie, et al.
Published: (2024)
Interpreting Neural Networks through Mahalanobis Distance
by: Oursland, Alan
Published: (2024)
by: Oursland, Alan
Published: (2024)
Training Neural Networks for Modularity aids Interpretability
by: Golechha, Satvik, et al.
Published: (2024)
by: Golechha, Satvik, et al.
Published: (2024)
The Interpretable and Effective Graph Neural Additive Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024)
by: Bechler-Speicher, Maya, et al.
Published: (2024)
Interpretable Neural Networks with Random Constructive Algorithm
by: Nan, Jing, et al.
Published: (2023)
by: Nan, Jing, et al.
Published: (2023)
Interpretable Graph Neural Networks for Tabular Data
by: Alkhatib, Amr, et al.
Published: (2023)
by: Alkhatib, Amr, et al.
Published: (2023)
Factor Graph-based Interpretable Neural Networks
by: Li, Yicong, et al.
Published: (2025)
by: Li, Yicong, et al.
Published: (2025)
A Comprehensive Survey on Self-Interpretable Neural Networks
by: Ji, Yang, et al.
Published: (2025)
by: Ji, Yang, et al.
Published: (2025)
Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopes
by: Abu-Haeyeh, Yasmine, et al.
Published: (2026)
by: Abu-Haeyeh, Yasmine, et al.
Published: (2026)
Left Atrial Segmentation with nnU-Net Using MRI
by: Hosseinabadi, Fatemeh, et al.
Published: (2025)
by: Hosseinabadi, Fatemeh, et al.
Published: (2025)
Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients
by: Wetzel, Sebastian Johann
Published: (2024)
by: Wetzel, Sebastian Johann
Published: (2024)
Attention Consistency Regularization for Interpretable Early-Exit Neural Networks
by: Zhao, Yanhua
Published: (2026)
by: Zhao, Yanhua
Published: (2026)
Efficient and Interpretable Neural Networks Using Complex Lehmer Transform
by: Ataei, Masoud, et al.
Published: (2025)
by: Ataei, Masoud, et al.
Published: (2025)
Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials
by: Furuta, Hiroki, et al.
Published: (2024)
by: Furuta, Hiroki, et al.
Published: (2024)
GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
by: Ranasinghe, Nisal, et al.
Published: (2024)
by: Ranasinghe, Nisal, et al.
Published: (2024)
Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey
by: Khan, Arham, et al.
Published: (2024)
by: Khan, Arham, et al.
Published: (2024)
Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
by: Wetzel, Sebastian J., et al.
Published: (2024)
by: Wetzel, Sebastian J., et al.
Published: (2024)
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
by: Lukyanov, Kirill, et al.
Published: (2025)
by: Lukyanov, Kirill, et al.
Published: (2025)
Neural Sum-of-Squares: Certifying the Nonnegativity of Polynomials with Transformers
by: Pelleriti, Nico, et al.
Published: (2025)
by: Pelleriti, Nico, et al.
Published: (2025)
Compositional Function Networks: A High-Performance Alternative to Deep Neural Networks with Built-in Interpretability
by: Li, Fang
Published: (2025)
by: Li, Fang
Published: (2025)
TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models
by: Tang, Yuchi, et al.
Published: (2025)
by: Tang, Yuchi, et al.
Published: (2025)
Neural Logic Networks for Interpretable Classification
by: Perreault, Vincent, et al.
Published: (2025)
by: Perreault, Vincent, et al.
Published: (2025)
Massive Activations in Graph Neural Networks: Decoding Attention for Domain-Dependent Interpretability
by: Bini, Lorenzo, et al.
Published: (2024)
by: Bini, Lorenzo, et al.
Published: (2024)
DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
by: Shing, Makoto, et al.
Published: (2025)
by: Shing, Makoto, et al.
Published: (2025)
Fitting Multilinear Polynomials for Logic Gate Networks
by: Kim, Youngsung
Published: (2026)
by: Kim, Youngsung
Published: (2026)
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior
by: Chan, Tsai Hor, et al.
Published: (2025)
by: Chan, Tsai Hor, et al.
Published: (2025)
SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs
by: Fang, Lanting, et al.
Published: (2024)
by: Fang, Lanting, et al.
Published: (2024)
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
by: Wang, Yuwen, et al.
Published: (2024)
by: Wang, Yuwen, et al.
Published: (2024)
FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series
by: Su, Qiqi, et al.
Published: (2023)
by: Su, Qiqi, et al.
Published: (2023)
GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series
by: Piao, Chengzhe, et al.
Published: (2024)
by: Piao, Chengzhe, et al.
Published: (2024)
Discovering Chunks in Neural Embeddings for Interpretability
by: Wu, Shuchen, et al.
Published: (2025)
by: Wu, Shuchen, et al.
Published: (2025)
KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems
by: Požgaj, Stjepan, et al.
Published: (2025)
by: Požgaj, Stjepan, et al.
Published: (2025)
E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis
by: Ma, Fei, et al.
Published: (2026)
by: Ma, Fei, et al.
Published: (2026)
MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
by: He, Jesse, et al.
Published: (2026)
by: He, Jesse, et al.
Published: (2026)
Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction
by: Li, Xiaodi, et al.
Published: (2024)
by: Li, Xiaodi, et al.
Published: (2024)
Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
by: Henderson, Edward, et al.
Published: (2025)
by: Henderson, Edward, et al.
Published: (2025)
Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
by: Lillo, Lute, et al.
Published: (2026)
by: Lillo, Lute, et al.
Published: (2026)
Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates
by: Gufran, Danish, et al.
Published: (2025)
by: Gufran, Danish, et al.
Published: (2025)
When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
by: Forest, Florent, et al.
Published: (2025)
by: Forest, Florent, et al.
Published: (2025)
Similar Items
-
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
by: Ranasinghe, Nisal, et al.
Published: (2023) -
Faithful Interpretation for Graph Neural Networks
by: Hu, Lijie, et al.
Published: (2024) -
Interpreting Neural Networks through Mahalanobis Distance
by: Oursland, Alan
Published: (2024) -
Training Neural Networks for Modularity aids Interpretability
by: Golechha, Satvik, et al.
Published: (2024) -
The Interpretable and Effective Graph Neural Additive Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024)