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The paper proposes ACES (Adaptive Coverage-aware Efficient Sampling), a structured sampling framework for implicit neural representations that decouples domain coverage from importance weighting via adaptive spatial partitions, achieving faster convergence and lower error on scientific field learning tasks.
This paper introduces CoroNeRF for joint reconstruction of 3D electron density and temperature fields from multiview solar observations, highlighting that image fidelity does not guarantee physical field fidelity and proposing seed-based error localization methods.
This paper develops NTK-KIP, MetaQuill, and MetaQuill-KIP algorithms to improve neural field reconstruction from sparse observations, making NTK-driven neural fields non-linear and meta-learnable for efficient few-shot adaptation.
This paper shows that the geometric symmetry visible from neural network weights depends on the positional encoding and readout observable, and validates this using MLPs trained on 2D signed distance functions with multiple symmetry groups.
This paper proposes Neural Radiated-Noise Fields (NRNF), a neural network approach for predicting underwater vehicle radiated noise spectra as a continuous function of 3D position, orientation, and frequency. Evaluated on lake trial data, the model achieves an average prediction error of 3.5 dB in the 50–5000 Hz band across multiple generalization settings.
This paper proposes NeTMY, an amortization-free coordinate neural field for inverse problems in NV-center quantum sensing, using a corrected forward model and sparse reconstruction losses to overcome center-collapse pathologies.