DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yun, Se-Young | - |
dc.contributor.advisor | 윤세영 | - |
dc.contributor.author | Go, Kyeong-Ryeol | - |
dc.date.accessioned | 2022-04-13T05:40:06Z | - |
dc.date.available | 2022-04-13T05:40:06Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=963738&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/292503 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : AI대학원, 2021.8,[iv, 30 p. :] | - |
dc.description.abstract | Meta-learning aims to learn a model that can handle multiple tasks generated from an unknown but shared distribution. However, typical meta-learning algorithms have assumed the tasks to be similar such that a single meta-learner is sufficient to aggregate the variations in all aspects. In addition, there has been less consideration on uncertainty when limited information is given as context. In this paper, we devise a novel meta-learning framework, called Meta-learning Amidst Heterogeneity and Ambiguity (MAHA), that outperforms previous works in terms of prediction based on its ability on task identification. By extensively conducting several experiments in regression and classification, we demonstrate the validity of our model, which turns out to be robust to both task heterogeneity and ambiguity. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Meta-learning▼aVariational inference▼aLatent representation▼aDisentanglement▼aInterpretability | - |
dc.subject | 메타 학습▼a변분 추론▼a잠재 표현▼a비엉킴▼a설명가능성 | - |
dc.title | Meta-learning amidst heterogeneity and ambiguity | - |
dc.title.alternative | 이질성과 모호성 가운데 설명가능한 메타 학습 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :AI대학원, | - |
dc.contributor.alternativeauthor | 고경렬 | - |
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