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Neurosymbolic Learning and Reasoning for Trustworthy AI
Neurosymbolic Learning and Reasoning for Trustworthy AI
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152016
- ISBN
- 9798382838519
- DDC
- 004
- 저자명
- Zeng, Zhe.
- 서명/저자
- Neurosymbolic Learning and Reasoning for Trustworthy AI
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 219 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Van den Broeck, Guy.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Along with the ubiquitous applications of Artificial Intelligence (AI), the quest for developing trustworthy AI models intensifies. Deep neural networks, while powerful in learning, fall short in reasoning with domain knowledge and offering robustness guarantees. Neurosymbolic AI bridges this gap by melding the learning capabilities of neural networks and reasoning techniques from symbolic AI, thus building models that behave as intended. This dissertation demonstrates my work that addresses the two fundamental challenges in neurosymbolic AI: 1) enabling differentiable learning of deep neural networks under symbolic constraints and 2) performing scalable and reliable probabilistic reasoning over expressive symbolic constraints. It presents how these neurosymbolic approaches achieve trustworthiness through explainability, uncertainty quantification, and domain-knowledge incorporation. These contributions enable broader applications of neurosymbolic AI in various domains including scientific discoveries.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 키워드
- Machine learning
- 키워드
- Neural networks
- 기타저자
- University of California, Los Angeles Computer Science 0201
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382838519
■035 ▼a(MiAaPQ)AAI31331728
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aZeng, Zhe.
■24510▼aNeurosymbolic Learning and Reasoning for Trustworthy AI
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a219 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Van den Broeck, Guy.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aAlong with the ubiquitous applications of Artificial Intelligence (AI), the quest for developing trustworthy AI models intensifies. Deep neural networks, while powerful in learning, fall short in reasoning with domain knowledge and offering robustness guarantees. Neurosymbolic AI bridges this gap by melding the learning capabilities of neural networks and reasoning techniques from symbolic AI, thus building models that behave as intended. This dissertation demonstrates my work that addresses the two fundamental challenges in neurosymbolic AI: 1) enabling differentiable learning of deep neural networks under symbolic constraints and 2) performing scalable and reliable probabilistic reasoning over expressive symbolic constraints. It presents how these neurosymbolic approaches achieve trustworthiness through explainability, uncertainty quantification, and domain-knowledge incorporation. These contributions enable broader applications of neurosymbolic AI in various domains including scientific discoveries.
■590 ▼aSchool code: 0031.
■650 4▼aComputer science
■650 4▼aComputer engineering
■653 ▼aMachine learning
■653 ▼aNeural networks
■653 ▼aSymbolic constraints
■653 ▼aDomain-knowledge incorporation
■690 ▼a0984
■690 ▼a0464
■690 ▼a0800
■71020▼aUniversity of California, Los Angeles▼bComputer Science 0201.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162470▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
