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Make Knowledge Computable: Towards Differentiable Neural-Symbolic AI- [electronic resource]
Make Knowledge Computable: Towards Differentiable Neural-Symbolic AI- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101452
- ISBN
- 9798379671976
- DDC
- 004
- 저자명
- Hu, Ziniu.
- 서명/저자
- Make Knowledge Computable: Towards Differentiable Neural-Symbolic AI - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Los Angeles., 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 1 online resource(207 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Sun, Yizhou.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약This thesis addresses the intersection of neural and symbolic artificial intelligence systems. Recent deep learning methods could memorize vast amount of world knowledge, but still have their limitation to conduct symbolic reasoning over them; while symbolic AI is good at solving reasoning tasks, but is inefficient for adapting to new knowledge. Prior efforts that bridge the two worlds mainly focus on building parsing-based systems, which require lots of annotated intermediate labels and hard to scale.My ultimate research goal is to enable neural model to interact with symbolic reasoning module in a differentiable manner, and train such Neural-Symbolic model end-to-end without intermediate labels. To bring this vision about, I have conducted works on:1. Designing Novel Reasoning Module: design differentiable neural modules that can conduct symbolic reasoning, including knowledge graph reasoning and complex Logical inference.2. Learning via Self-Supervision: train the neural model via self-supervision from structural and symbolic knowledge base without additional annotation.3. Generalizing across Domains: the modular design of neural-symbolic system by its nature help to generalize better for Out-of-Distribution, Out-of-Vocabulary, cross-lingual and cross-type.Putting these pieces together, I am pursuing the ultimate vision to build end-to-end Neural-Symbolic system that has the capacity of reasoning, advancing to true human intelligence.
- 일반주제명
- Computer science.
- 키워드
- Data mining
- 키워드
- Deep learning
- 키워드
- Knowledge graph
- 키워드
- Machine learning
- 기타저자
- University of California, Los Angeles Computer Science 0201
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101452
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■007cr#unu||||||||
■020 ▼a9798379671976
■035 ▼a(MiAaPQ)AAI30526201
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aHu, Ziniu.
■24510▼aMake Knowledge Computable: Towards Differentiable Neural-Symbolic AI▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Los Angeles. ▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a1 online resource(207 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Sun, Yizhou.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThis thesis addresses the intersection of neural and symbolic artificial intelligence systems. Recent deep learning methods could memorize vast amount of world knowledge, but still have their limitation to conduct symbolic reasoning over them; while symbolic AI is good at solving reasoning tasks, but is inefficient for adapting to new knowledge. Prior efforts that bridge the two worlds mainly focus on building parsing-based systems, which require lots of annotated intermediate labels and hard to scale.My ultimate research goal is to enable neural model to interact with symbolic reasoning module in a differentiable manner, and train such Neural-Symbolic model end-to-end without intermediate labels. To bring this vision about, I have conducted works on:1. Designing Novel Reasoning Module: design differentiable neural modules that can conduct symbolic reasoning, including knowledge graph reasoning and complex Logical inference.2. Learning via Self-Supervision: train the neural model via self-supervision from structural and symbolic knowledge base without additional annotation.3. Generalizing across Domains: the modular design of neural-symbolic system by its nature help to generalize better for Out-of-Distribution, Out-of-Vocabulary, cross-lingual and cross-type.Putting these pieces together, I am pursuing the ultimate vision to build end-to-end Neural-Symbolic system that has the capacity of reasoning, advancing to true human intelligence.
■590 ▼aSchool code: 0031.
■650 4▼aComputer science.
■653 ▼aData mining
■653 ▼aDeep learning
■653 ▼aKnowledge graph
■653 ▼aMachine learning
■653 ▼aNatural language processing
■653 ▼aNeural-Symbolic AI
■690 ▼a0984
■690 ▼a0800
■71020▼aUniversity of California, Los Angeles▼bComputer Science 0201.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933889▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
