Small-scale photonic KANs using four-parameter telecom nonlinear modules achieve 94.3% accuracy on classification and R²=0.986 on regression with few modules, approaching software baselines.
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Koopman theory plus knowledge distillation yields linearized models from pre-trained nets that outperform standard least-squares Koopman approximations on MNIST and Fashion-MNIST in accuracy and stability.
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Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules
Small-scale photonic KANs using four-parameter telecom nonlinear modules achieve 94.3% accuracy on classification and R²=0.986 on regression with few modules, approaching software baselines.