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Automated Machine Learning in the Era of Large Foundation Models
Automated Machine Learning in the Era of Large Foundation Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153126
- ISBN
- 9798346855552
- DDC
- 004
- 저자명
- Wang, Ruochen.
- 서명/저자
- Automated Machine Learning in the Era of Large Foundation Models
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 130 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Hsieh, Cho-Jui;Wang, Wei.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Intelligence, one of the most profound phenomena on Earth, has evolved over 600 million years, transforming from simple neural systems into human cognition capable of unraveling universal mysteries and creating silicon-based intelligence. This evolutionary process, with its inherent drive towards increasing complexity, seemingly defies thermodynamic principles, suggesting the existence of self-evolving mechanisms in life. Recreating such mechanisms within artificial systems is a crucial milestone on the path toward Artificial General Intelligence (AGI).Automated Machine Learning (AutoML) represents a significant step in this direction. By enabling AI systems to optimize their own design processes, AutoML reformulates machine learning pipeline construction as a search problem, automating the selection of architectures, optimizers, hyperparameters, and even reasoning paths from an expansive search space. Despite its current limitations, AutoML has already demonstrated remarkable success in advancing machine learning across diverse applications.This thesis delves into the synergistic interplay between AutoML and large foundation models, particularly the recent breakthroughs in large-scale generative models like large language models (LLMs) and diffusion models. These models exhibit emergent behaviors, highlighting machine learning systems as complex entities where scaling produces unpredictable and potentially transformative capabilities. We emphasize the application of AutoML in automating the design of training and inference processes, crucial for the continued advancement of these models.Ultimately, we aspire that this research contributes a meaningful step towards the ambitious pursuit of Artificial General Intelligence (AGI).
- 일반주제명
- Computer science
- 일반주제명
- Systems science
- 키워드
- Diffusion models
- 키워드
- Search problem
- 기타저자
- University of California, Los Angeles Computer Science 0201
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153126
■006m o d
■007cr#unu||||||||
■020 ▼a9798346855552
■035 ▼a(MiAaPQ)AAI31765001
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aWang, Ruochen.
■24510▼aAutomated Machine Learning in the Era of Large Foundation Models
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a130 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Hsieh, Cho-Jui;Wang, Wei.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aIntelligence, one of the most profound phenomena on Earth, has evolved over 600 million years, transforming from simple neural systems into human cognition capable of unraveling universal mysteries and creating silicon-based intelligence. This evolutionary process, with its inherent drive towards increasing complexity, seemingly defies thermodynamic principles, suggesting the existence of self-evolving mechanisms in life. Recreating such mechanisms within artificial systems is a crucial milestone on the path toward Artificial General Intelligence (AGI).Automated Machine Learning (AutoML) represents a significant step in this direction. By enabling AI systems to optimize their own design processes, AutoML reformulates machine learning pipeline construction as a search problem, automating the selection of architectures, optimizers, hyperparameters, and even reasoning paths from an expansive search space. Despite its current limitations, AutoML has already demonstrated remarkable success in advancing machine learning across diverse applications.This thesis delves into the synergistic interplay between AutoML and large foundation models, particularly the recent breakthroughs in large-scale generative models like large language models (LLMs) and diffusion models. These models exhibit emergent behaviors, highlighting machine learning systems as complex entities where scaling produces unpredictable and potentially transformative capabilities. We emphasize the application of AutoML in automating the design of training and inference processes, crucial for the continued advancement of these models.Ultimately, we aspire that this research contributes a meaningful step towards the ambitious pursuit of Artificial General Intelligence (AGI).
■590 ▼aSchool code: 0031.
■650 4▼aComputer science
■650 4▼aSystems science
■653 ▼aAutomated Machine Learning
■653 ▼aLarge language models
■653 ▼aDiffusion models
■653 ▼aLarge foundation models
■653 ▼aSearch problem
■690 ▼a0984
■690 ▼a0800
■690 ▼a0790
■71020▼aUniversity of California, Los Angeles▼bComputer Science 0201.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17165118▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
