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ChebMixer: Efficient Graph Representation Learning with MLP Mixer
In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens.
Efficient Deformable Shape Correspondence via Multiscale Spectral Manifold Wavelets Preservation
The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes.
Shape correspondence using anisotropic Chebyshev spectral CNNs
Rather than using fixed oriented kernels in the spatial domain in previous CNNs, in our framework, the kernels are learned by spectral filtering, based on the eigen-decompositions of multiple Anisotropic Laplace-Beltrami Operators.
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