A Sugeno Integral View of Binarized Neural Network Inference
In this article, we establish a precise connection between binarized neural networks (BNNs) and Sugeno integrals. The advantage of the Sugeno integral is that it provides a framework for representing the importance of inputs and their interactions, while being equivalent to a set of if-then rules. For a hidden BNN neuron at inference time, we show that the activation threshold test can be written as a Sugeno integral on binary inputs. This yields an explicit set-function representation of each neuron decision, and an associated rule-based representation. We also provide a Sugeno-integral expression for the last-layer score. Finally, we discuss how the same framework can be adapted to supp...
| Authors | Ismaïl Baaj, Henri Prade |
|---|---|
| Primary category | cs.AI |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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