NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
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Abstract (arXiv)
In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure. This new framework aims to explore additional desirable properties of artificial neurons. We show that some specializations of NEWRON allow the network to be interpretable with no change in their expressiveness. By just inspecting the models produced by our NEWRON-based networks, we can understand the rules governing the task. Extensive experiments show that the quality of the generated models is better than traditional interpretable models and in line or better than standard neural networks.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 5 Oct 2021.
Builds on
10 of 21 references.
References · 10
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2017Interpretation of Neural Networks is Fragilecited by 443
- 2018Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neuronscited by 235
- 2018What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Modelscited by 124
- 2019A Survey of Optimization Methods from a Machine Learning Perspectivecited by 90
- 2020A survey on modern trainable activation functionscited by 89
- 2018Deep Neural Decision Treescited by 72
- 2019Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networkscited by 49
- 2018NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networkscited by 31
- 2017A New Type of Neurons for Machine Learningcited by 16
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Citing papers per year: 2022: 1.
1 paper.