The Neural Tangent Kernel for Classification
In wide neural networks, the Neural Tangent Kernel (NTK) remains approximately constant during training, providing a powerful theoretical tool for studying training dynamics, generalization, and connections to kernel methods. However, this theory is largely restricted to regression losses. It was previously thought that training on a classification loss, or more generally losses involving nonlinear output transformations, breaks this property, leading to divergent logits and a breakdown of the linearization. In this paper, we extend NTK theory to classification by identifying conditions under which wide neural networks remain in the lazy training regime. We show that parameter-space regul...
| Authors | Jonathan Plenk, Sergio Calvo-Ordonez, Alvaro Cartea et al. |
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| Primary category | cs.LG |
| Published | 17 May 2026 |
| Last updated at source | 22 May 2026 |
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