New bounds reveal configuration-dependent limits for shallow ReLU networks on spheres

Researchers have established new theoretical bounds for the approximation capabilities of shallow ReLU$^k$ neural networks when operating on a sphere. The findings indicate that the approximation accuracy is dependent on the configuration of the network's inner parameters, specifically the antipodal separation distance. For certain parameter configurations, the networks can outperform traditional finite element methods, but this advantage has inherent limitations. AI

RANK_REASON This is a research paper published on arXiv detailing theoretical findings about neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

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