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Orthogonal Transforms in Neural Networks Amount to Effective Regularization

Krzysztof Zaj\k{a}cWojciech SopotPawe{\l} Wachel
Feb 2024
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摘要原文
We consider applications of neural networks in nonlinear system identification and formulate a hypothesis that adjusting general network structure by incorporating frequency information or other known orthogonal transform, should result in an efficient neural network retaining its universal properties. We show that such a structure is a universal approximator and that using any orthogonal transform in a proposed way implies regularization during training by adjusting the learning rate of each parameter individually. We empirically show in particular, that such a structure, using the Fourier transform, outperforms equivalent models without orthogonality support.
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