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Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy Machine Learning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy Machine Learning. |
| 개인저자 | Xu, Kaidi. |
| 단체저자명 | Northeastern University. Electrical and Computer Engineering. |
| 발행사항 | [S.l.]: Northeastern University., 2021. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2021. |
| 형태사항 | 116 p. |
| 기본자료 저록 | Dissertations Abstracts International 83-02B. Dissertation Abstract International |
| ISBN | 9798535511139 |
| 학위논문주기 | Thesis (Ph.D.)--Northeastern University, 2021. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Advisor: Lin, Xue. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 일반주제명 | Computer engineering. Computer science. Information technology. Artificial intelligence. Sparsity. Internships. Deep learning. Datasets. Success. Dissertations & theses. Noise. Advisors. Defense. Performance evaluation. COVID-19. Power. Experiments. Neural networks. Medical research. Classification. Linear programming. Natural language processing. Methods. Algorithms. Ablation. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
