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Low-Rank RNN Adaptation for Context-Aware Language Modeling
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Jaech, Aaron. |
| 단체저자명 | University of Washington. Electrical Engineering. |
| 서명/저자사항 | Low-Rank RNN Adaptation for Context-Aware Language Modeling. |
| 발행사항 | [S.l.] : University of Washington., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 124 p. |
| 소장본 주기 | School code: 0250. |
| ISBN | 9780438178175 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Mari Ostendorf. |
| 요약 | A long-standing weakness of statistical language models is that their performance drastically degrades if they are used on data that varies even slightly from the data on which they were trained. In practice, applications require the use of adap |
| 요약 | The current standard approach to recurrent neural network language model adaptation is to apply a simple linear shift to the recurrent and/or output layer bias vector. Although this is helpful, it does not go far enough. This thesis introduces a |
| 요약 | In our experiments on several different datasets and multiple types of context, the increased adaptation of the recurrent layer is always helpful, as measured by perplexity, the standard for evaluating language models. We also demonstrate impact |
| 일반주제명 | Computer science. Statistics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14999220 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00028529 | 004 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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