Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients
Fuente:
arXiv
Guardado en:
| Autor principal: | Wetzel, Sebastian Johann |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
por: Wetzel, Sebastian J., et al.
Publicado: (2024)
por: Wetzel, Sebastian J., et al.
Publicado: (2024)
Interpretable Machine Learning in Physics: A Review
por: Wetzel, Sebastian Johann, et al.
Publicado: (2025)
por: Wetzel, Sebastian Johann, et al.
Publicado: (2025)
Deriving Equivalent Symbol-Based Decision Models from Feedforward Neural Networks
por: Seidel, Sebastian, et al.
Publicado: (2025)
por: Seidel, Sebastian, et al.
Publicado: (2025)
Symbol Correctness in Deep Neural Networks Containing Symbolic Layers
por: Bembenek, Aaron, et al.
Publicado: (2024)
por: Bembenek, Aaron, et al.
Publicado: (2024)
Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression
por: Montazerin, Mansooreh, et al.
Publicado: (2025)
por: Montazerin, Mansooreh, et al.
Publicado: (2025)
Gradient-Based Program Synthesis with Neurally Interpreted Languages
por: Macfarlane, Matthew V., et al.
Publicado: (2026)
por: Macfarlane, Matthew V., et al.
Publicado: (2026)
Interpretable Neural-Symbolic Concept Reasoning
por: Barbiero, Pietro, et al.
Publicado: (2023)
por: Barbiero, Pietro, et al.
Publicado: (2023)
Operator Feature Neural Network for Symbolic Regression
por: Deng, Yusong, et al.
Publicado: (2024)
por: Deng, Yusong, et al.
Publicado: (2024)
Interpretable and Fair Mechanisms for Abstaining Classifiers
por: Lenders, Daphne, et al.
Publicado: (2025)
por: Lenders, Daphne, et al.
Publicado: (2025)
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression
por: Wu, Min, et al.
Publicado: (2024)
por: Wu, Min, et al.
Publicado: (2024)
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression
por: Acero, Fernando, et al.
Publicado: (2024)
por: Acero, Fernando, et al.
Publicado: (2024)
Distilling Symbolic Priors for Concept Learning into Neural Networks
por: Marinescu, Ioana, et al.
Publicado: (2024)
por: Marinescu, Ioana, et al.
Publicado: (2024)
Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression
por: Hu, Jiao, et al.
Publicado: (2024)
por: Hu, Jiao, et al.
Publicado: (2024)
Gradient Routing: Masking Gradients to Localize Computation in Neural Networks
por: Cloud, Alex, et al.
Publicado: (2024)
por: Cloud, Alex, et al.
Publicado: (2024)
Axiomatization of Gradient Smoothing in Neural Networks
por: Zhou, Linjiang, et al.
Publicado: (2024)
por: Zhou, Linjiang, et al.
Publicado: (2024)
Faithful Interpretation for Graph Neural Networks
por: Hu, Lijie, et al.
Publicado: (2024)
por: Hu, Lijie, et al.
Publicado: (2024)
Emergent Symbol-like Number Variables in Artificial Neural Networks
por: Grant, Satchel, et al.
Publicado: (2025)
por: Grant, Satchel, 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)
Interpretable by AI Mother Tongue: Native Symbolic Reasoning in Neural Models
por: Liu, Hung Ming
Publicado: (2025)
por: Liu, Hung Ming
Publicado: (2025)
Gradient Inversion Attack on Graph Neural Networks
por: Sinha, Divya Anand, et al.
Publicado: (2024)
por: Sinha, Divya Anand, et al.
Publicado: (2024)
GradINN: Gradient Informed Neural Network
por: Aglietti, Filippo, et al.
Publicado: (2024)
por: Aglietti, Filippo, et al.
Publicado: (2024)
Gradient-Free Training of Quantized Neural Networks
por: Cohen, Noa, et al.
Publicado: (2024)
por: Cohen, Noa, et al.
Publicado: (2024)
Vertical Symbolic Regression via Deep Policy Gradient
por: Jiang, Nan, et al.
Publicado: (2024)
por: Jiang, Nan, et al.
Publicado: (2024)
Interpreting Neural Networks through Mahalanobis Distance
por: Oursland, Alan
Publicado: (2024)
por: Oursland, Alan
Publicado: (2024)
Training Neural Networks for Modularity aids Interpretability
por: Golechha, Satvik, et al.
Publicado: (2024)
por: Golechha, Satvik, et al.
Publicado: (2024)
The Interpretable and Effective Graph Neural Additive Networks
por: Bechler-Speicher, Maya, et al.
Publicado: (2024)
por: Bechler-Speicher, Maya, et al.
Publicado: (2024)
Interpretable Neural Networks with Random Constructive Algorithm
por: Nan, Jing, et al.
Publicado: (2023)
por: Nan, Jing, et al.
Publicado: (2023)
Interpretable Graph Neural Networks for Tabular Data
por: Alkhatib, Amr, et al.
Publicado: (2023)
por: Alkhatib, Amr, et al.
Publicado: (2023)
Factor Graph-based Interpretable Neural Networks
por: Li, Yicong, et al.
Publicado: (2025)
por: Li, Yicong, et al.
Publicado: (2025)
Closed-Form Concept Erasure via Double Projections
por: Zhang, Chi, et al.
Publicado: (2026)
por: Zhang, Chi, et al.
Publicado: (2026)
Uncertainty Quantification for Gradient-based Explanations in Neural Networks
por: Mulye, Mihir, et al.
Publicado: (2024)
por: Mulye, Mihir, et al.
Publicado: (2024)
SURGE: Surrogate Gradient Adaptation in Binary Neural Networks
por: Huang, Haoyu, et al.
Publicado: (2026)
por: Huang, Haoyu, et al.
Publicado: (2026)
Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms
por: Pavasovic, Krunoslav Lehman, et al.
Publicado: (2025)
por: Pavasovic, Krunoslav Lehman, et al.
Publicado: (2025)
A Comprehensive Survey on Self-Interpretable Neural Networks
por: Ji, Yang, et al.
Publicado: (2025)
por: Ji, Yang, et al.
Publicado: (2025)
Bridging Philosophy and Machine Learning: A Structuralist Framework for Classifying Neural Network Representations
por: Culcu, Yildiz
Publicado: (2025)
por: Culcu, Yildiz
Publicado: (2025)
Understanding Gradient Boosting Classifier: Training, Prediction, and the Role of $γ_j$
por: Chen, Hung-Hsuan
Publicado: (2024)
por: Chen, Hung-Hsuan
Publicado: (2024)
Do Deep Neural Network Solutions Form a Star Domain?
por: Sonthalia, Ankit, et al.
Publicado: (2024)
por: Sonthalia, Ankit, et al.
Publicado: (2024)
Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets
por: Cohen, Khen, et al.
Publicado: (2024)
por: Cohen, Khen, et al.
Publicado: (2024)
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
por: Li, Wenqiang, et al.
Publicado: (2023)
por: Li, Wenqiang, et al.
Publicado: (2023)
Gradient Flow Convergence Guarantee for General Neural Network Architectures
por: Jakhmola, Yash
Publicado: (2025)
por: Jakhmola, Yash
Publicado: (2025)
Ejemplares similares
-
Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
por: Wetzel, Sebastian J., et al.
Publicado: (2024) -
Interpretable Machine Learning in Physics: A Review
por: Wetzel, Sebastian Johann, et al.
Publicado: (2025) -
Deriving Equivalent Symbol-Based Decision Models from Feedforward Neural Networks
por: Seidel, Sebastian, et al.
Publicado: (2025) -
Symbol Correctness in Deep Neural Networks Containing Symbolic Layers
por: Bembenek, Aaron, et al.
Publicado: (2024) -
Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression
por: Montazerin, Mansooreh, et al.
Publicado: (2025)