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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CHOIR uses coordinated harmonic superposition instead of function composition and perceptual spectrum calibration to improve stability and reduce bias in implicit neural representations for multi-dimensional data recovery.
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.
citing papers explorer
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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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Calibrated Harmonic Overlaid Implicit Neural Representations for Multi-Dimensional Data
CHOIR uses coordinated harmonic superposition instead of function composition and perceptual spectrum calibration to improve stability and reduce bias in implicit neural representations for multi-dimensional data recovery.
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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.