A technique plants exact fixed points in Neural-ODE velocity fields with a rigorous proof that universality is preserved under local constraints.
arXiv preprint arXiv:2304.10552 , year=
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Diagonal plus Low-Rank (DLoR) neural networks achieve universal approximation for general activations by additive or multiplicative decompositions of full-rank transformations.
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Exact Fixed-Point Constraints in Neural-ODEs with Provable Universality
A technique plants exact fixed points in Neural-ODE velocity fields with a rigorous proof that universality is preserved under local constraints.
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Structural Correspondence and Universal Approximation in Diagonal plus Low-Rank Neural Networks
Diagonal plus Low-Rank (DLoR) neural networks achieve universal approximation for general activations by additive or multiplicative decompositions of full-rank transformations.