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Discovering and Exploiting Structure for Gaussian Processes
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Discovering and Exploiting Structure for Gaussian Processes. |
| 개인저자 | Gardner, Jacob Ross. |
| 단체저자명 | Cornell University. Computer Science. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 123 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438026575 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Kilian Q. Weinberger. |
| 요약 | Gaussian processes have emerged as a powerful tool for modeling complex and noisy functions. They have found wide applicability in personalized medicine, time series analysis, prediction tasks in the physical sciences, and recently blackbox opti |
| 요약 | Despite these two clear advantages, some of the most popular applications of Gaussian processes have focused on exploiting the first advantage of GPs, and very little on exploiting the latter. As an example, in Bayesian optimization, off-the-she |
| 요약 | In this thesis, we will demonstrate by way of application that the second advantage can be just as critical as the first. By leveraging expert medical knowledge, we develop a GP model that exploits basic facts about human hearing to dramatically |
| 일반주제명 | Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
