DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Hwang, Sung Ju | - |
dc.contributor.advisor | 황성주 | - |
dc.contributor.author | Huh, Jawook | - |
dc.date.accessioned | 2023-06-23T19:34:44Z | - |
dc.date.available | 2023-06-23T19:34:44Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1030600&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/309281 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전산학부, 2023.2,[v, 53 p. :] | - |
dc.description.abstract | As the usage of deep learning models rapidly grows in real-world industries, the need for time-series explainable modeling increases in order to support human's complex decision-making process. In this thesis, I introduce practical problems that arise when applying explainable models to real-world industries and discuss how uncertainty modeling, active learning, counterfactual inference-based approaches tackle these challenges with three perspectives. First, to tackle the limited learning environment with quantity and quality of real-world data, I introduce uncertainty-aware network that provides reliable future prediction and explanations that considers the notion of uncertainty. Second, in order to improve model explainability which agrees more with real-world practitioners, I propose an interactive attention learning framework. Lastly, I extend the conventional explainable approach to scenario-based explainable framework which provides scenario-based forecasting explanations based on the condition of desired counterfactual questions, which enables to help complex decision-making process of real-world practitioners by providing actionable information. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Time-series forecasting▼aExplainable AI▼aActive learning▼aUncertainty modeling | - |
dc.subject | 시계열 예측▼a설명가능 인공지능▼a불확실성 모델링▼a능동 학습 | - |
dc.title | Time-series explainable artificial intelligence for real-world applications | - |
dc.title.alternative | 실제 산업에 활용하는 시계열 설명가능 인공지능 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전산학부, | - |
dc.contributor.alternativeauthor | 허자욱 | - |
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