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Interpretable Machine Learning with Applications in Neuroscience
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Abbasi Asl, Reza. |
| 단체저자명 | University of California, Berkeley. Electrical Engineering & Computer Sciences. |
| 서명/저자사항 | Interpretable Machine Learning with Applications in Neuroscience. |
| 발행사항 | [S.l.] : University of California, Berkeley., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 104 p. |
| 소장본 주기 | School code: 0028. |
| ISBN | 9780438325272 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Bin Yu. |
| 요약 | In the past decade, research in machine learning has been principally focused on the development of algorithms and models with high predictive capabilities. Models such as convolutional neural networks (CNNs) achieve state-of-the-art predictive |
| 요약 | In this thesis, we investigate two regimes based on (1) compression and (2) stability to build more interpretable machine learning models. These regimes will be demonstrated in a computational neuroscience study. In the first part of the thesis, |
| 요약 | In the second part of this thesis, we introduce DeepTune, a stability-driven visualization and interpretation framework for CNN-based models. DeepTune is used to characterize biological neurons in the V4 area of the primate visual cortex. V4 is |
| 요약 | In the final part of this thesis, we study the application of CAR and RAR compressed CNNs in modeling V4 neurons. Both CAR and RAR compression give rise to a new set of simpler models for V4 neurons with similar accuracy to existing state-of-the |
| 일반주제명 | Electrical engineering. Computer science. Neurosciences. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International80-01B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998426 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00027791 | 621.3 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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