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Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy Machine Learning
Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy ...
Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy Machine Learning

Detailed Information

자료유형  
 학위논문파일 국외
ISBN  
9798535511139
DDC  
621.3
저자명  
Xu, Kaidi.
서명/저자  
Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy Machine Learning
발행사항  
[Sl] : Northeastern University, 2021
발행사항  
Ann Arbor : ProQuest Dissertations & Theses, 2021
형태사항  
116 p
주기사항  
Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
주기사항  
Advisor: Lin, Xue.
학위논문주기  
Thesis (Ph.D.)--Northeastern University, 2021.
사용제한주기  
This item must not be sold to any third party vendors.
일반주제명  
Computer engineering
일반주제명  
Computer science
일반주제명  
Information technology
일반주제명  
Artificial intelligence
일반주제명  
Sparsity
일반주제명  
Internships
일반주제명  
Deep learning
일반주제명  
Datasets
일반주제명  
Success
일반주제명  
Dissertations & theses
일반주제명  
Noise
일반주제명  
Advisors
일반주제명  
Defense
일반주제명  
Performance evaluation
일반주제명  
COVID-19
일반주제명  
Power
일반주제명  
Experiments
일반주제명  
Neural networks
일반주제명  
Medical research
일반주제명  
Classification
일반주제명  
Linear programming
일반주제명  
Natural language processing
일반주제명  
Methods
일반주제명  
Algorithms
일반주제명  
Ablation
키워드  
Adversarial Machine Learning
키워드  
AI Security
키워드  
Deep Learning
키워드  
Trustworthy Machine Learning
기타저자  
Northeastern University Electrical and Computer Engineering
기본자료저록  
Dissertations Abstracts International. 83-02B.
기본자료저록  
Dissertation Abstract International
전자적 위치 및 접속  
로그인 후 원문을 볼 수 있습니다.

MARC

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■020    ▼a9798535511139
■035    ▼a(MiAaPQ)AAI28646717
■040    ▼aMiAaPQ▼cMiAaPQ
■0820  ▼a621.3
■1001  ▼aXu,  Kaidi.
■24510▼aCan  We  Trust  AI?  Towards  Practical  Implementation  and  Theoretical  Analysis  in  Trustworthy  Machine  Learning
■260    ▼a[Sl]▼bNortheastern  University▼c2021
■260  1▼aAnn  Arbor▼bProQuest  Dissertations  &  Theses▼c2021
■300    ▼a116  p
■500    ▼aSource:  Dissertations  Abstracts  International,  Volume:  83-02,  Section:  B.
■500    ▼aAdvisor:  Lin,  Xue.
■5021  ▼aThesis  (Ph.D.)--Northeastern  University,  2021.
■506    ▼aThis  item  must  not  be  sold  to  any  third  party  vendors.
■590    ▼aSchool  code:  0160.
■650  4▼aComputer  engineering
■650  4▼aComputer  science
■650  4▼aInformation  technology
■650  4▼aArtificial  intelligence
■650  4▼aSparsity
■650  4▼aInternships
■650  4▼aDeep  learning
■650  4▼aDatasets
■650  4▼aSuccess
■650  4▼aDissertations  &  theses
■650  4▼aNoise
■650  4▼aAdvisors
■650  4▼aDefense
■650  4▼aPerformance  evaluation
■650  4▼aCOVID-19
■650  4▼aPower
■650  4▼aExperiments
■650  4▼aNeural  networks
■650  4▼aMedical  research
■650  4▼aClassification
■650  4▼aLinear  programming
■650  4▼aNatural  language  processing
■650  4▼aMethods
■650  4▼aAlgorithms
■650  4▼aAblation
■653    ▼aAdversarial  Machine  Learning
■653    ▼aAI  Security
■653    ▼aDeep  Learning
■653    ▼aTrustworthy  Machine  Learning
■690    ▼a0464
■690    ▼a0489
■690    ▼a0984
■690    ▼a0800
■71020▼aNortheastern  University▼bElectrical  and  Computer  Engineering.
■7730  ▼tDissertations  Abstracts  International▼g83-02B.
■773    ▼tDissertation  Abstract  International
■790    ▼a0160
■791    ▼aPh.D.
■792    ▼a2021
■793    ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16053335▼nKERIS▼z이  자료의  원문은  한국교육학술정보원에서  제공합니다.
■980    ▼a202202▼f2022

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