We study the fundamental problem of PAC learning positive linear combinations of $k$
arbitrary ReLU activations on $\mathbb{R}^d$ with respect to the Gaussian distribution, in $L_2$
loss. We give the first fully-polynomial time algorithm for this learning task.
Specifically, our algorithm draws $\mathrm{poly}(d, k, 1/\epsilon)$ samples,
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