Neural networks prioritize amplitude over phase in Fourier space during training on translation-invariant data; power-law spectra accelerate phase learning despite not aiding classification.
et al.SGD on Neural Networks Learns Functions of Increasing ComplexityinAdvances in Neural Information Processing Systems32(2019), 3491–3501
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A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
Neural networks prioritize amplitude over phase in Fourier space during training on translation-invariant data; power-law spectra accelerate phase learning despite not aiding classification.