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Deep Learning for Inverse Problems in Engineering and Science- [electronic resource]
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Deep Learning for Inverse Problems in Engineering and Science- [electronic resource]
Detailed Information
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100454
- ISBN
- 9798379604028
- DDC
- 621.3
- 서명/저자
- Deep Learning for Inverse Problems in Engineering and Science - [electronic resource]
- 발행사항
- [S.l.]: : Harvard University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(235 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Ba, Demba.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약In a famous Socratic dialogue by Plato, Meno postulates that the epistemic pursuit of knowledge demands a target, without which one cannot determine the object of inquiry or even recognize it upon discovery. The paradox resonates with the enigmatic, blackbox nature of deep learning; the abundance of data in fields such as computer vision and natural language processing and increasingly massive computational power has perhaps impeded a thorough understanding of these machines. We may fall victim to Meno's Paradox, namely the inability to reap the full benefits of deep learning's remarkable capabilities for engineering and science at large. To date, deep learning applications are fairly unexplored in data-scarce scientific and engineering fields with rich mathematical grounding such as the theory of optimization for solving inverse problems, or those in which interpretability matters. In such domains, the goal often goes beyond data fitting, and extends to advancing scientific discoveries. In this context, this dissertation imposes an inductive bias on deep neural networks to discover human-understandable patterns for science and improve the efficiency and performance in unsupervised or data-scarce inverse problems in engineering.
- 일반주제명
- Electrical engineering.
- 일반주제명
- Computer engineering.
- 키워드
- Deep learning
- 키워드
- Inverse problems
- 키워드
- Sparse coding
- 기타저자
- Harvard University Engineering and Applied Sciences - Engineering Sciences
- 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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■00520240214100454
■006m o d
■007cr#unu||||||||
■020 ▼a9798379604028
■035 ▼a(MiAaPQ)AAI30492248
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aTolooshams, Bahareh.▼0(orcid)0000-0002-5955-6535
■24510▼aDeep Learning for Inverse Problems in Engineering and Science▼h[electronic resource]
■260 ▼a[S.l.]:▼bHarvard University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(235 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Ba, Demba.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aIn a famous Socratic dialogue by Plato, Meno postulates that the epistemic pursuit of knowledge demands a target, without which one cannot determine the object of inquiry or even recognize it upon discovery. The paradox resonates with the enigmatic, blackbox nature of deep learning; the abundance of data in fields such as computer vision and natural language processing and increasingly massive computational power has perhaps impeded a thorough understanding of these machines. We may fall victim to Meno's Paradox, namely the inability to reap the full benefits of deep learning's remarkable capabilities for engineering and science at large. To date, deep learning applications are fairly unexplored in data-scarce scientific and engineering fields with rich mathematical grounding such as the theory of optimization for solving inverse problems, or those in which interpretability matters. In such domains, the goal often goes beyond data fitting, and extends to advancing scientific discoveries. In this context, this dissertation imposes an inductive bias on deep neural networks to discover human-understandable patterns for science and improve the efficiency and performance in unsupervised or data-scarce inverse problems in engineering.
■590 ▼aSchool code: 0084.
■650 4▼aElectrical engineering.
■650 4▼aComputer engineering.
■653 ▼aComputational neuroscience
■653 ▼aDeep learning
■653 ▼aDictionary learning
■653 ▼aInverse problems
■653 ▼aSparse coding
■690 ▼a0544
■690 ▼a0464
■690 ▼a0800
■71020▼aHarvard University▼bEngineering and Applied Sciences - Engineering Sciences.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16932404▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
