Small photonic KANs using commodity telecom nonlinear modules reach 98.4% accuracy on nonlinear classification with only four modules and remain robust to hardware impairments.
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.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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
-
Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules
Small photonic KANs using commodity telecom nonlinear modules reach 98.4% accuracy on nonlinear classification with only four modules and remain robust to hardware impairments.
-
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.
-
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.