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On Priors for Bayesian Neural Networks
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Nalisnick, Eric Thomas. |
| 단체저자명 | University of California, Irvine. Computer Science. |
| 서명/저자사항 | On Priors for Bayesian Neural Networks. |
| 발행사항 | [S.l.] : University of California, Irvine., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 156 p. |
| 소장본 주기 | School code: 0030. |
| ISBN | 9780438296503 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Padhraic Smyth. |
| 요약 | Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, speech recognition, and natural language processing. However, neural networks still have deficiencies. For instance, they have a penchant to over |
| 요약 | Bayesian inference is characterized by specification of the prior distribution, and unfortunately, choosing priors for neural networks is difficult. The primary obstacle is that the weights have no intuitive interpretation and seemingly sensible |
| 일반주제명 | Artificial intelligence. Statistics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International80-01B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998739 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00028110 | 001.5 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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