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math.PR2021
Testing thresholds for high-dimensional sparse random geometric graphs
Siqi Liu, Sidhanth Mohanty, Tselil Schramm +1
In the random geometric graph model , we identify each of our vertices with an independently and uniformly sampled vector from the -dimensional unit sph…
math.PR2021★ 5 cited
Non-asymptotic approximations of neural networks by Gaussian processes
Ronen Eldan, Dan Mikulincer, Tselil Schramm
We study the extent to which wide neural networks may be approximated by Gaussian processes when initialized with random weights. It is a well-established fact that as the width of…