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.
Memristor-based spiking neural networks: cooperative development of neural network architecture/algorithms and memristors
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
Spin wave-based physical reservoir computing achieves 85.8% speaker classification accuracy without cochleagram preprocessing.
citing papers explorer
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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.
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Extraction of linearized models from pre-trained networks via knowledge distillation
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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Spoken Digit Recognition and Speaker Classification by Nonlinear Interfered Spin Wave-Based Physical Reservoir Computing
Spin wave-based physical reservoir computing achieves 85.8% speaker classification accuracy without cochleagram preprocessing.