서브메뉴
검색
Perception and Reasoning in Chaotic and Uncertain Environments
Perception and Reasoning in Chaotic and Uncertain Environments
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104819
- ISBN
- 9798293893140
- DDC
- 004
- 저자명
- Gupta, Ritwik.
- 서명/저자
- Perception and Reasoning in Chaotic and Uncertain Environments
- 발행사항
- [Sl] : University of California, Berkeley, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 204 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-04, Section: B.
- 주기사항
- Advisor: Darrell, Trevor;Sastry, S. Shankar.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 초록/해제
- 요약Perception in chaotic and uncertain environments, such as those marred by disasters and wars, remains a fundamental challenge in artificial intelligence. Characterized by data that is simultaneously partially observable, noisy, multi-spectral, extremely large, and with constantly shifting data biases, these environments undermine the assumptions on which most modern vision systems are built. This dissertation introduces a series of methodological contributions to address these challenges, including novel frameworks for representation learning under variable resolution, efficient processing of gigapixel imagery, and sequence modeling in high-dimensional visual domains. Alongside these technical advances, the work examines the governance implications of dual-use AI systems, proposing data-centric approaches to regulation and open-source methods for capability assessment. Together, these contributions aim to inform both the design and oversight of AI systems deployed in operationally complex and high-stakes settings.
- 일반주제명
- Computer science
- 일반주제명
- Public policy
- 키워드
- Perception
- 키워드
- Reasoning
- 기타저자
- University of California, Berkeley Electrical Engineering & Computer Sciences
- 기본자료저록
- Dissertations Abstracts International. 87-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017358996
■00520260202104819
■006m o d
■007cr#unu||||||||
■020 ▼a9798293893140
■035 ▼a(MiAaPQ)AAI32169028
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aGupta, Ritwik.
■24510▼aPerception and Reasoning in Chaotic and Uncertain Environments
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a204 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-04, Section: B.
■500 ▼aAdvisor: Darrell, Trevor;Sastry, S. Shankar.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aPerception in chaotic and uncertain environments, such as those marred by disasters and wars, remains a fundamental challenge in artificial intelligence. Characterized by data that is simultaneously partially observable, noisy, multi-spectral, extremely large, and with constantly shifting data biases, these environments undermine the assumptions on which most modern vision systems are built. This dissertation introduces a series of methodological contributions to address these challenges, including novel frameworks for representation learning under variable resolution, efficient processing of gigapixel imagery, and sequence modeling in high-dimensional visual domains. Alongside these technical advances, the work examines the governance implications of dual-use AI systems, proposing data-centric approaches to regulation and open-source methods for capability assessment. Together, these contributions aim to inform both the design and oversight of AI systems deployed in operationally complex and high-stakes settings.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science
■650 4▼aPublic policy
■653 ▼aPerception
■653 ▼aGigapixel imagery
■653 ▼aReasoning
■690 ▼a0800
■690 ▼a0984
■690 ▼a0630
■71020▼aUniversity of California, Berkeley▼bElectrical Engineering & Computer Sciences.
■7730 ▼tDissertations Abstracts International▼g87-04B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358996▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
