Exploiting Subgradient Sparsity in Max-Plus Neural Networks
Deep Neural Networks are powerful tools for solving machine learning problems, but their training often involves dense and costly parameter updates. In this work, we use a novel Max-Plus neural architecture in which classical addition and multiplication are replaced with maximum and summation operations respectively. This is a promising architecture in terms of interpretability, but its training is challenging. A particular feature is that this algebraic structure naturally induces sparsity in the subgradients, as only neurons that contribute to the maximum affect the loss. However, standard backpropagation fails to exploit this sparsity, leading to unnecessary computations. In this work,...
| Authors | Ikhlas Enaieh, Olivier Fercoq |
|---|---|
| Primary category | stat.ML |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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