A carefully designed two-layer neural network with channel attention trained by gradient descent achieves the minimax optimal sample complexity Theta(d to the ell_0 over epsilon) for learning degree-ell_0 spherical polynomials.
A Bound on Tail Probabilities for Quadratic Forms in Independent Random Variables Whose Distributions are not Necessarily Symmetric
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Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Feature Learning by Learnable Channel Attention
A carefully designed two-layer neural network with channel attention trained by gradient descent achieves the minimax optimal sample complexity Theta(d to the ell_0 over epsilon) for learning degree-ell_0 spherical polynomials.