Neural tangent kernel from neural reparameterization modulates sensitivity and wave tangent kernels to produce spectral filtering, wavenumber modulation, and frequency bias that improve NeurFWI convergence.
Toward a better understanding of fourier neural operators from a spectral perspective
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Multi-stage residual learning in quantum circuits mitigates frequency parameterization bias and improves test MSE on synthetic benchmarks with multiple localized frequency components compared to single-stage training.
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Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel
Neural tangent kernel from neural reparameterization modulates sensitivity and wave tangent kernels to produce spectral filtering, wavenumber modulation, and frequency bias that improve NeurFWI convergence.
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Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning
Multi-stage residual learning in quantum circuits mitigates frequency parameterization bias and improves test MSE on synthetic benchmarks with multiple localized frequency components compared to single-stage training.
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