We studied the approximation and computation efficiency of nonlinear approximation via compositions, especially when the dictionary D L consists of deep ...
Feb 26, 2019 · Nonlinear approximation seeks the linear combination of the best N terms \{T_n\}_{1\le n\le N}\subseteq{\cal D} to approximate a given function f with the ...
Nonlinear approximation seeks the linear combination of the best terms to approximate a given function with the minimum approximation error.
NONLINEAR APPROXIMATION VIA COMPOSITIONS. ∗. 1. ZUOWEI SHEN† , HAIZHAO YANG‡ , AND SHIJUN ZHANG§. 2. Abstract. Given a function dictionary D and an ...
Jul 10, 2019 · Motivated by recent success of deep learning, we propose dictionaries with functions in a form of compositions, i.e.,.
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We further quantify the improvement of the best N-term approximation rate in terms of N when L is increased from 1 to 2 or 3 to show the power of compositions.
This work derives a novel approximation error bound with an explicit prefactor for Sobolev-regular functions using deep convolutional neural networks (CNNs) ...
Aug 29, 2019 · Given a function dictionary D and an approximation budget N∈N, nonlinear approximation seeks the linear combination of the best N terms ...
This is a survey of nonlinear approximation, especially that part of the sub- ject which is important in numerical computation. Nonlinear approximation.
Missing: Compositions. | Show results with:Compositions.
Nov 5, 2020 · We studied the approximation and computation efficiency of nonlinear approximation via compositions, especially when the dictionary DL ...