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Machine Learning in the Real World with Multiple Objectives
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Machine Learning in the Real World with Multiple Objectives. |
| 개인저자 | Bolukbasi, Tolga. |
| 단체저자명 | Boston University. Electrical & Computer Engineering ENG. |
| 발행사항 | [S.l.]: Boston University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 169 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438149977 |
| 학위논문주기 | Thesis (Ph.D.)--Boston University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Venkatesh Saligrama. |
| 요약 | Machine learning (ML) is ubiquitous in many real-world applications. Existing ML systems are based on optimizing a single quality metric such as prediction accuracy. These metrics typically do not fully align with real-world design constraints s |
| 요약 | First, we focus on decreasing the test-time computational costs of prediction systems. Budget constraints arise in many machine learning problems. Computational costs limit the usage of many models on small devices such as IoT or mobile phones a |
| 요약 | In the context of fairness, we first demonstrate that a naive application of ML methods runs the risk of amplifying social biases present in data. This danger is particularly acute for methods based on word embeddings, which are increasingly gai |
| 일반주제명 | Electrical engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
