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cs.LG2021
Neural Network Approximation of Refinable Functions
Ingrid Daubechies, Ronald DeVore, Nadav Dym +6
In the desire to quantify the success of neural networks in deep learning and other applications, there is a great interest in understanding which functions are efficiently approxi…
math.ST2021★ 1 cited
Convergence rates of vector-valued local polynomial regression
Yariv Aizenbud, Barak Sober
Non-parametric estimation of functions as well as their derivatives by means of local-polynomial regression is a subject that was studied in the literature since the late 1970's. G…
math.ST2021
Non-Parametric Estimation of Manifolds from Noisy Data
Yariv Aizenbud, Barak Sober
A common observation in data-driven applications is that high dimensional data has a low intrinsic dimension, at least locally. In this work, we consider the problem of estimating…