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We give a pseudorandom generator that fools degree-$d$ polynomial threshold functions over $n$-dimensional Gaussian space with seed length $mathrm{poly}(d)cdot log n$. All previous generators had a seed length with at least a $2^d$ dependence on $d$. The key new ingredient is a Local Hyperconcentration Theorem, which shows that every degree-$d$ Gaussian polynomial is hyperconcentrated almost everywhere at scale $d^{-O(1)}$.
A polynomial threshold function (PTF) $f:mathbb{R}^n rightarrow mathbb{R}$ is a function of the form $f(x) = mathsf{sign}(p(x))$ where $p$ is a polynomial of degree at most $d$. PTFs are a classical and well-studied complexity class with applications
We give a pseudorandom generator that fools $m$-facet polytopes over ${0,1}^n$ with seed length $mathrm{polylog}(m) cdot log n$. The previous best seed length had superlinear dependence on $m$. An immediate consequence is a deterministic quasipolynom
Say that A is a Hadamard factorization of the identity I_n of size n if the entrywise product of A and the transpose of A is I_n. It can be easily seen that the rank of any Hadamard factorization of the identity must be at least sqrt{n}. Dietzfelbing
LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions. When autonomous vehicles are sending LiDAR point clouds to deep networks for perception and
An $ntimes n$ matrix $M$ is called a textit{fooling-set matrix of size $n$} if its diagonal entries are nonzero and $M_{k,ell} M_{ell,k} = 0$ for every $k e ell$. Dietzfelbinger, Hromkovi{v{c}}, and Schnitger (1996) showed that $n le (mbox{rk} M)^2$,