RepNN reparameterizes the first hidden layer of DNNs to enable adaptive frequency scaling, improving accuracy on oscillatory and multiscale functions with minimal extra cost.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FPRO applies Frenet-frame RL with curvature-torsion manufacturability constraints and PPO optimization to produce collision-free, fabricable pipe paths for aeroengines, outperforming Cartesian and baseline RL methods in experiments and real fabrication.
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RepNN: Tackling spectral bias in deep neural networks via parameter reparameterization
RepNN reparameterizes the first hidden layer of DNNs to enable adaptive frequency scaling, improving accuracy on oscillatory and multiscale functions with minimal extra cost.
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Design for Manufacturing: A Manufacturability Knowledge-Integrated Reinforcement Learning Framework for Free-Form Pipe Routing in Aeroengines
FPRO applies Frenet-frame RL with curvature-torsion manufacturability constraints and PPO optimization to produce collision-free, fabricable pipe paths for aeroengines, outperforming Cartesian and baseline RL methods in experiments and real fabrication.