Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
PulseAugur coverage of Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains — every cluster mentioning Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains across labs, papers, and developer communities, ranked by signal.
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New research details spectral convergence of Random Feature Method in multiple dimensions
Researchers have demonstrated the spectral convergence of the Random Feature Method (RFM) for multidimensional targets across various regularity classes, including Sobolev, Gevrey, and ultra-analytic. The analysis provi…
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Quantum SEDONet advances neural operator networks for PDEs
Researchers have developed Quantum SEDONet, an advancement in quantum deep operator networks designed to solve partial differential equations. This new model embeds spectral bases, such as Fourier or Chebyshev features,…
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New neural representation method enhances 3D gravity inversion
Researchers have developed a novel unsupervised method for 3D gravity inversion using depth-aware implicit neural representations. This approach represents the subsurface density volume with multiple neural networks opt…