mlsanityData to 7 Oct 2026

Explainable Neural Networks based on Additive Index Models

arXiv 1806.01933 ↗First posted 5 Jun 2018stat.ML cs.LGcited by 33

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Abstract (arXiv)

Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much research in developing various approaches to understand the model behavior. In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features. Unlike fully connected neural networks, the features engineered by the xNN can be extracted from the network in a relatively straightforward manner and the results displayed. With appropriate regularization, the xNN provides a parsimonious explanation of the relationship between the features and the output. We illustrate this interpretable feature--engineering property on simulated examples.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2018.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 7; 2024: 6; 2025: 4; 2026: 1.

33 papers.

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  1. 2021How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanationscited by 58
  2. 2021Defining and Quantifying the Emergence of Sparse Concepts in DNNscited by 36
  3. 2023Does a Neural Network Really Encode Symbolic Concepts?cited by 27
  4. 2023Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Lifecited by 19
  5. 2021Model Explainability in Deep Learning Based Natural Language Processingcited by 14
  6. 2021LocalGLMnet: interpretable deep learning for tabular datacited by 14
  7. 2024Towards the Dynamics of a DNN Learning Symbolic Interactionscited by 13
  8. 2022A Survey of Neural Treescited by 7
  9. 2022GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintscited by 6
  10. 2023Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisonscited by 4
  11. 2021Bias, Fairness, and Accountability with AI and ML Algorithmscited by 3
  12. 2022Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Modelscited by 3
21 more
  1. 2023On marginal feature attributions of tree-based modelscited by 3
  2. 2025FAME: Introducing Fuzzy Additive Models for Explainable AIcited by 3
  3. 2023Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and featurescited by 2
  4. 2024An AI Architecture with the Capability to Explain Recognition Resultscited by 2
  5. 2024Improving Network Interpretability via Explanation Consistency Evaluationcited by 2
  6. 2024Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classificationcited by 2
  7. 2021Towards interpreting ML-based automated malware detection models: a surveycited by 1
  8. 2023Monotone Tree-Based GAMI Models by Adapting XGBoostcited by 1
  9. 2024Are Logistic Models Really Interpretable?cited by 1
  10. 2024An AI Architecture with the Capability to Classify and Explain Hardware Trojanscited by 1
  11. 2021Neural Networks and Denotationcited by 0
  12. 2021Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Modelcited by 0
  13. 2022Semantic interpretation for convolutional neural networks: What makes a cat a cat?cited by 0
  14. 2022Interpretable Feature Engineering for Time Series Predictors using Attention Networkscited by 0
  15. 2022Deep Explainable Learning with Graph Based Data Assessing and Rule Reasoningcited by 0
  16. 2022Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learningcited by 0
  17. 2023On the explainability of quantum neural networks based on variational quantum circuitscited by 0
  18. 2025Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictabilitycited by 0
  19. 2025Onto-Epistemological Analysis of AI Explanationscited by 0
  20. 2025When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregatecited by 0
  21. 2026Explainability Methods for Hardware Trojan Detection: A Systematic Comparisoncited by 0