DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS

Ph.D

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書目詳細資料
主要作者: ZHANG SHIJUN
其他作者: MATHEMATICS
格式: Theses and Dissertations
語言:English
出版: 2021
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在線閱讀:https://scholarbank.nus.edu.sg/handle/10635/186064
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spelling sg-nus-scholar.10635-1860642024-10-26T16:51:47Z DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS ZHANG SHIJUN MATHEMATICS Zuowei Shen Yang Haizhao Function Composition, Deep Neural Network, Approximation Theory, Floor or ReLU Activation Function, Exponential Convergence, Polynomial Approximation Ph.D DOCTOR OF PHILOSOPHY (FOS) 2021-02-01T18:01:37Z 2021-02-01T18:01:37Z 2020-08-04 Thesis ZHANG SHIJUN (2020-08-04). DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS. ScholarBank@NUS Repository. https://scholarbank.nus.edu.sg/handle/10635/186064 0000-0003-4115-7891 en
institution National University of Singapore
building NUS Library
continent Asia
country Singapore
Singapore
content_provider NUS Library
collection ScholarBank@NUS
language English
topic Function Composition, Deep Neural Network, Approximation Theory, Floor or ReLU Activation Function, Exponential Convergence, Polynomial Approximation
spellingShingle Function Composition, Deep Neural Network, Approximation Theory, Floor or ReLU Activation Function, Exponential Convergence, Polynomial Approximation
ZHANG SHIJUN
DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS
description Ph.D
author2 MATHEMATICS
author_facet MATHEMATICS
ZHANG SHIJUN
format Theses and Dissertations
author ZHANG SHIJUN
author_sort ZHANG SHIJUN
title DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS
title_short DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS
title_full DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS
title_fullStr DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS
title_full_unstemmed DEEP NEURAL NETWORK APPROXIMATION VIA FUNCTION COMPOSITIONS
title_sort deep neural network approximation via function compositions
publishDate 2021
url https://scholarbank.nus.edu.sg/handle/10635/186064
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