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.
arXiv preprint arXiv:2505.15222 , year=
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DisINR improves INR medical reconstruction by disentangling shared and subject-specific representations, pre-training the shared modules from raw data via differentiable forward models, and freezing them at test time.
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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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Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction
DisINR improves INR medical reconstruction by disentangling shared and subject-specific representations, pre-training the shared modules from raw data via differentiable forward models, and freezing them at test time.