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Specification-Guided Generative Models
Specification-Guided Generative Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152004
- ISBN
- 9798384022589
- DDC
- 004
- 저자명
- Young, Halley.
- 서명/저자
- Specification-Guided Generative Models
- 발행사항
- [Sl] : University of Pennsylvania, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 163 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Bastani, Osbert.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2024.
- 초록/해제
- 요약We introduce Specification-Guided Generative Models (SGMs) - a novel approach to generative models - and illustrate their versatility and effectiveness across various domains. SGMs represent an evolution in model control and expressiveness, providing a framework for augmenting existing models to achieve improved quality, controllability, and variety in generated content. By developing methods for extracting latent structures and conditioning on these structures, we refine generative processes and tailor outputs to user preferences. We demonstrate SGMs' applicability within the image, music, and poetry domains, showing how SGMs can be adapted and applied to a range of generative tasks. Finally, we establish how integrating specifications, interpretable structures, and stochastic sampling techniques helps create AI systems more closely aligned with human creativity and expressiveness.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 키워드
- Diversity
- 키워드
- Neurosymbolic
- 키워드
- Human creativity
- 키워드
- Poetry domains
- 기타저자
- University of Pennsylvania Computer and Information Science
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384022589
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aYoung, Halley.
■24510▼aSpecification-Guided Generative Models
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a163 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Bastani, Osbert.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2024.
■520 ▼aWe introduce Specification-Guided Generative Models (SGMs) - a novel approach to generative models - and illustrate their versatility and effectiveness across various domains. SGMs represent an evolution in model control and expressiveness, providing a framework for augmenting existing models to achieve improved quality, controllability, and variety in generated content. By developing methods for extracting latent structures and conditioning on these structures, we refine generative processes and tailor outputs to user preferences. We demonstrate SGMs' applicability within the image, music, and poetry domains, showing how SGMs can be adapted and applied to a range of generative tasks. Finally, we establish how integrating specifications, interpretable structures, and stochastic sampling techniques helps create AI systems more closely aligned with human creativity and expressiveness.
■590 ▼aSchool code: 0175.
■650 4▼aComputer science
■650 4▼aComputer engineering
■653 ▼aDiversity
■653 ▼aNeurosymbolic
■653 ▼aHuman creativity
■653 ▼aPoetry domains
■653 ▼aSpecification-Guided Generative Models
■690 ▼a0984
■690 ▼a0464
■690 ▼a0800
■71020▼aUniversity of Pennsylvania▼bComputer and Information Science.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162367▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
