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Exploiting Program Representation with Shader Applications- [electronic resource]
Inhalt Info
Exploiting Program Representation with Shader Applications- [electronic resource]
자료유형  
 학위논문
Control Number  
0016932194
International Standard Book Number  
9798379716820
Dewey Decimal Classification Number  
004
Main Entry-Personal Name  
Yang, Yuting.
Publication, Distribution, etc. (Imprint  
[S.l.] : Princeton University., 2023
Publication, Distribution, etc. (Imprint  
Ann Arbor : ProQuest Dissertations & Theses, 2023
Physical Description  
1 online resource(243 p.)
General Note  
Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
General Note  
Advisor: Finkelstein, Adam.
Dissertation Note  
Thesis (Ph.D.)--Princeton University, 2023.
Restrictions on Access Note  
This item must not be sold to any third party vendors.
Summary, Etc.  
요약Programs are widely used in content creation. For example, artists design shader programs to procedurally render scenes and textures, while musicians construct "synth" programs to generate electronic sound. While the generated content is typically the focus of attention, the programs themselves offer hidden potential for transformations that can support untapped applications. In this dissertation, we will discuss four projects that exploit the program structure to automatically apply machine learning or math transformations as if they were manually designed by domain experts. First, we describe a compiler-based framework with novel math rules to extend reverse mode automatic differentiation so as to provide accurate gradients for arbitrary discontinuous programs. The differentiation framework allows us to optimize procedural shader parameters to match target images. Second, we extend the differentiation framework to audio "synth" programs so as to match the acoustic properties of a provided sound clip. We next propose a compiler framework to automatically approximate the convolution of an arbitrary program with a Gaussian kernel in order to smooth the program for visual antialiasing. Finally, we explore the benefit of program representation in deep-learning tasks by proposing to learn from program traces of procedural fragment shaders - programs that generate images. In each of these settings, we demonstrate the benefit of exploiting the program structure to generalize hand-crafted techniques to arbitrary programs.
Subject Added Entry-Topical Term  
Computer science.
Subject Added Entry-Topical Term  
Computer engineering.
Index Term-Uncontrolled  
Automatic differentiation
Index Term-Uncontrolled  
Compiler
Index Term-Uncontrolled  
Computer graphics
Index Term-Uncontrolled  
Differentiable rendering
Index Term-Uncontrolled  
Machine learning
Index Term-Uncontrolled  
Programming language
Added Entry-Corporate Name  
Princeton University Computer Science
Host Item Entry  
Dissertations Abstracts International. 84-12B.
Host Item Entry  
Dissertation Abstract International
Electronic Location and Access  
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Control Number  
joongbu:643937
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