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On Priors for Bayesian Neural Networks
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | On Priors for Bayesian Neural Networks. |
| 개인저자 | Nalisnick, Eric Thomas. |
| 단체저자명 | University of California, Irvine. Computer Science. |
| 발행사항 | [S.l.]: University of California, Irvine., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 156 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438296503 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Irvine, 2018. |
| 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 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
