A new research paper published on arXiv explores the approximation of analytic functions using ReLU neural networks. The study introduces a characterization that jointly considers network depth and width, moving beyond single-parameter analyses. The findings indicate that for analytic functions, network depth plays a more significant role than width, yielding approximation rates of \mathcal{O}\left(N^{-C L^{\tau}}\right). The paper details technical challenges and employs refined constructions for approximating power functions, multiplication, and polynomials. AI
IMPACT This research provides theoretical insights into the approximation capabilities of ReLU networks, potentially influencing future model architectures.
RANK_REASON The cluster contains an academic paper detailing theoretical advancements in neural network approximation.
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