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Bridging the Connectrion Between Deep Learning and Stochastic Optimal Control
Bridging the Connectrion Between Deep Learning and Stochastic Optimal Control
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105556
- ISBN
- 9798265405975
- DDC
- 006.3
- 저자명
- Chen, Tianrong.
- 서명/저자
- Bridging the Connectrion Between Deep Learning and Stochastic Optimal Control
- 발행사항
- [Sl] : Georgia Institute of Technology, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 207 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Theodorou, Evangelos A.;Bloch, Matthieu R.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
- 초록/해제
- 요약Generative models have gained significant popularity in recent years, and Stochastic Optimal Control soc has also advanced rapidly in parallel. This thesis addresses the problem of understanding dynamical generative models from the perspective of Stochastic Optimal Control, thereby providing insights from the well-established Stochastic Optimal Control theory. Additionally, it explores the challenges of high-dimensional Stochastic Optimal Control by leveraging deep learning techniques. Through this dual approach, the research aims to enhance the theoretical understanding and practical application of generative models and Stochastic Optimal Control in complex, high-dimensional environments.
- 일반주제명
- Diffusion models
- 일반주제명
- Deep learning
- 일반주제명
- Neural networks
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798265405975
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■035 ▼a(MiAaPQ)GeorgiaTech76857
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a006.3
■1001 ▼aChen, Tianrong.
■24510▼aBridging the Connectrion Between Deep Learning and Stochastic Optimal Control
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a207 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Theodorou, Evangelos A.;Bloch, Matthieu R.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2024.
■520 ▼aGenerative models have gained significant popularity in recent years, and Stochastic Optimal Control soc has also advanced rapidly in parallel. This thesis addresses the problem of understanding dynamical generative models from the perspective of Stochastic Optimal Control, thereby providing insights from the well-established Stochastic Optimal Control theory. Additionally, it explores the challenges of high-dimensional Stochastic Optimal Control by leveraging deep learning techniques. Through this dual approach, the research aims to enhance the theoretical understanding and practical application of generative models and Stochastic Optimal Control in complex, high-dimensional environments.
■590 ▼aSchool code: 0078.
■650 4▼aDiffusion models
■650 4▼aDeep learning
■650 4▼aNeural networks
■690 ▼a0800
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360615▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
