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Provable and Practical Algorithms for Non-Convex Problems in Machine Learning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Provable and Practical Algorithms for Non-Convex Problems in Machine Learning. |
| 개인저자 | Yuan, Yang. |
| 단체저자명 | Cornell University. Computer Science. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 204 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438026636 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Robert David Kleinberg. |
| 요약 | Machine learning has become one of the most exciting research areas in the world, with various applications. However, there exists a noticeable gap between theory and practice. On one hand, a simple algorithm like stochastic gradient descent (SG |
| 요약 | This dissertation is about bridging the gap between theory and practice from two directions. The first direction is "practice to theory", i.e., to explain and analyze the existing algorithms and empirical observations in machine learning. Along |
| 요약 | The other direction is "theory to practice", i.e., using theoretical tools to obtain new, better and practical algorithms. Along this direction, we introduce a new algorithm Harmonica that uses Fourier analysis and compressed sensing for tuning |
| 일반주제명 | Computer science. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
