activity
20182022
most citedCramér Type Moderate Deviations for Random Fields

13 citations · 14 across the 5 of their papers we have counts for

collaborators

6 papers

math.ST2022

On the integrated mean squared error of wavelet density estimation for linear processes

Aleksandr Beknazaryan, Hailin Sang, Peter Adamic

Let be a linear process with density function . We study wavelet density estimation of . Under some regular conditions on the characterist…

stat.ML2022

Shallow neural network representation of polynomials

Aleksandr Beknazaryan

We show that -variate polynomials of degree can be represented on as shallow neural networks of width . Also, by SNN representation of localized Taylor p…

stat.ML2021

Neural networks with superexpressive activations and integer weights

Aleksandr Beknazaryan

An example of an activation function is given such that networks with activations , integer weights and a fixed architecture depending on approx…

stat.ML2021

Function approximation by deep neural networks with parameters

Aleksandr Beknazaryan

In this paper it is shown that -smooth functions can be approximated by deep neural networks with ReLU activation function and with parameters $\{0,\pm \frac{1}{2}, \pm 1, 2\}…

math.ST201913 cited

Cramér Type Moderate Deviations for Random Fields

Aleksandr Beknazaryan, Hailin Sang, Yimin Xiao

We study the Cramér type moderate deviation for partial sums of random fields by applying the conjugate method. The results are applicable to the partial sums of linear random fiel…

math.ST20181 cited

On mutual information estimation for mixed-pair random variables

Aleksandr Beknazaryan, Xin Dang, Hailin Sang

We study the mutual information estimation for mixed-pair random variables. One random variable is discrete and the other one is continuous. We develop a kernel method to estimate…