A fixed-width, shared-activation deep network architecture is constructed so that every intermediate readout approximates the target function at a geometrically decaying rate proportional to depth.
Fourier multi-component and multi-layer neural networks: Unlocking high-frequency potential
2 Pith papers cite this work. Polarity classification is still indexing.
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The Neural Basis Method uses a predefined neural basis space and operator residual metric to deliver accurate single solves and fast parametric learning for multiscale Darcian dynamics.
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Layer-wise Geometric Approximation Rates for Deep Networks
A fixed-width, shared-activation deep network architecture is constructed so that every intermediate readout approximates the target function at a geometrically decaying rate proportional to depth.
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Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method
The Neural Basis Method uses a predefined neural basis space and operator residual metric to deliver accurate single solves and fast parametric learning for multiscale Darcian dynamics.