NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing.IEEE Signal Processing Magazine, 38(2):18–44
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i-DEQ adds momentum to DEQ fixed-point iterations, yielding convergence guarantees, training stability, and halved inference time while matching state-of-the-art reconstruction quality on inverse problems.
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Neural Fields for NV-Center Inverse Sensing
NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
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i-DEQ: A stable inertial deep equilibrium model for image restoration
i-DEQ adds momentum to DEQ fixed-point iterations, yielding convergence guarantees, training stability, and halved inference time while matching state-of-the-art reconstruction quality on inverse problems.