Interpreting Deep Neural Networks with the Package innsight
Fuente:
arXiv
Saved in:
| Main Authors: | Koenen, Niklas, Wright, Marvin N. |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution
by: Koenen, Niklas, et al.
Published: (2024)
by: Koenen, Niklas, et al.
Published: (2024)
Gradient-based Explanations for Deep Learning Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2025)
by: Langbein, Sophie Hanna, et al.
Published: (2025)
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2026)
by: Langbein, Sophie Hanna, et al.
Published: (2026)
Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
by: Blesch, Kristin, et al.
Published: (2025)
by: Blesch, Kristin, et al.
Published: (2025)
Machine Learning in Epidemiology
by: Wright, Marvin N., et al.
Published: (2026)
by: Wright, Marvin N., et al.
Published: (2026)
Imputation Uncertainty in Interpretable Machine Learning Methods
by: Golchian, Pegah, et al.
Published: (2025)
by: Golchian, Pegah, et al.
Published: (2025)
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
by: Kapar, Jan, et al.
Published: (2025)
by: Kapar, Jan, et al.
Published: (2025)
Fast Estimation of Partial Dependence Functions using Trees
by: Liu, Jinyang, et al.
Published: (2024)
by: Liu, Jinyang, et al.
Published: (2024)
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials
by: Morala, Pablo, et al.
Published: (2024)
by: Morala, Pablo, et al.
Published: (2024)
Interpretable Machine Learning for Survival Analysis
by: Langbein, Sophie Hanna, et al.
Published: (2024)
by: Langbein, Sophie Hanna, et al.
Published: (2024)
Towards Interpretable Deep Neural Networks for Tabular Data
by: Elhadri, Khawla, et al.
Published: (2025)
by: Elhadri, Khawla, et al.
Published: (2025)
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
by: Chen, Yuanyuan, et al.
Published: (2021)
by: Chen, Yuanyuan, et al.
Published: (2021)
How Interpretable Are Interpretable Graph Neural Networks?
by: Chen, Yongqiang, et al.
Published: (2024)
by: Chen, Yongqiang, et al.
Published: (2024)
Physics-informed Neural Networks with Unknown Measurement Noise
by: Pilar, Philipp, et al.
Published: (2022)
by: Pilar, Philipp, et al.
Published: (2022)
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)
Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
by: Hu, Xuran, et al.
Published: (2024)
by: Hu, Xuran, et al.
Published: (2024)
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)
Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks
by: Pilar, Philipp, et al.
Published: (2025)
by: Pilar, Philipp, et al.
Published: (2025)
Scalable Subsampling Inference for Deep Neural Networks
by: Wu, Kejin, et al.
Published: (2024)
by: Wu, Kejin, et al.
Published: (2024)
On the Interpretability of Quantum Neural Networks
by: Pira, Lirandë, et al.
Published: (2023)
by: Pira, Lirandë, et al.
Published: (2023)
CONFINE: Conformal Prediction for Interpretable Neural Networks
by: Huang, Linhui, et al.
Published: (2024)
by: Huang, Linhui, et al.
Published: (2024)
Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task
by: Maleki, Alireza, et al.
Published: (2024)
by: Maleki, Alireza, et al.
Published: (2024)
Missing value imputation with adversarial random forests -- MissARF
by: Golchian, Pegah, et al.
Published: (2025)
by: Golchian, Pegah, et al.
Published: (2025)
Decomposing Global Feature Effects Based on Feature Interactions
by: Herbinger, Julia, et al.
Published: (2023)
by: Herbinger, Julia, et al.
Published: (2023)
neuralGAM: An R Package for Fitting Generalized Additive Neural Networks
by: Ortega-Fernandez, Ines, et al.
Published: (2025)
by: Ortega-Fernandez, Ines, et al.
Published: (2025)
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
by: Arya, Shreyash, et al.
Published: (2024)
by: Arya, Shreyash, et al.
Published: (2024)
Interpreting Temporal Graph Neural Networks with Koopman Theory
by: Guerra, Michele, et al.
Published: (2024)
by: Guerra, Michele, et al.
Published: (2024)
Interpretable Graph Neural Networks for Heterogeneous Tabular Data
by: Alkhatib, Amr, et al.
Published: (2024)
by: Alkhatib, Amr, et al.
Published: (2024)
Seeking Interpretability and Explainability in Binary Activated Neural Networks
by: Leblanc, Benjamin, et al.
Published: (2022)
by: Leblanc, Benjamin, et al.
Published: (2022)
Graph Structure Learning with Interpretable Bayesian Neural Networks
by: Wasserman, Max, et al.
Published: (2024)
by: Wasserman, Max, et al.
Published: (2024)
Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
by: Lucena, Anaísa, et al.
Published: (2026)
by: Lucena, Anaísa, et al.
Published: (2026)
GPEX, A Framework For Interpreting Artificial Neural Networks
by: Akbarnejad, Amir, et al.
Published: (2021)
by: Akbarnejad, Amir, et al.
Published: (2021)
On the Neural Feature Ansatz for Deep Neural Networks
by: Tansley, Edward, et al.
Published: (2025)
by: Tansley, Edward, et al.
Published: (2025)
Faithful Interpretation for Graph Neural Networks
by: Hu, Lijie, et al.
Published: (2024)
by: Hu, Lijie, et al.
Published: (2024)
xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
by: Burk, Lukas, et al.
Published: (2026)
by: Burk, Lukas, et al.
Published: (2026)
Graph Neural Networks Are Not Continuous Across Graph Resolutions
by: Koke, Christian, et al.
Published: (2026)
by: Koke, Christian, et al.
Published: (2026)
dnamite: A Python Package for Neural Additive Models
by: Van Ness, Mike, et al.
Published: (2025)
by: Van Ness, Mike, et al.
Published: (2025)
Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders
by: Marks, Luke, et al.
Published: (2024)
by: Marks, Luke, et al.
Published: (2024)
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
by: Schmalwasser, Laines, et al.
Published: (2025)
by: Schmalwasser, Laines, et al.
Published: (2025)
On the Stability of Neural Networks in Deep Learning
by: Delattre, Blaise
Published: (2025)
by: Delattre, Blaise
Published: (2025)
Similar Items
-
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution
by: Koenen, Niklas, et al.
Published: (2024) -
Gradient-based Explanations for Deep Learning Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2025) -
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2026) -
Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
by: Blesch, Kristin, et al.
Published: (2025) -
Machine Learning in Epidemiology
by: Wright, Marvin N., et al.
Published: (2026)