June 5, 2024, 4:43 a.m. | Madison Cooley, Shandian Zhe, Robert M. Kirby, Varun Shankar

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02336v1 Announce Type: new
Abstract: We present polynomial-augmented neural networks (PANNs), a novel machine learning architecture that combines deep neural networks (DNNs) with a polynomial approximant. PANNs combine the strengths of DNNs (flexibility and efficiency in higher-dimensional approximation) with those of polynomial approximation (rapid convergence rates for smooth functions). To aid in both stable training and enhanced accuracy over a variety of problems, we present (1) a family of orthogonality constraints that impose mutual orthogonality between the polynomial and the …

abstract approximation architecture arxiv constraints convergence cs.lg efficiency flexibility function machine machine learning networks neural networks novel polynomial type

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