DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Park, Jinkyoo | - |
dc.contributor.advisor | 박진규 | - |
dc.contributor.author | Yoon, Kanghoon | - |
dc.date.accessioned | 2022-04-21T19:31:14Z | - |
dc.date.available | 2022-04-21T19:31:14Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=963731&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/295314 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2021.8,[iii, 19 p. :] | - |
dc.description.abstract | We propose the graph recurrent temporal point process (GRTPP), a deep learning model that can estimate simultaneously the probabilities of occurrence for multiple events in continuous time. GRTPP represents the history of multiple events with their event occurring times into a sequence of graphs and embeds the sequence of graphs into node embeddings for each event. Using the node embedding for each event type, GRTPP then estimates the conditional intensity function for the corresponding event by using a neural network. By approximately integrating the estimated intensity functions, GRTPP computes the likelihood of events used to train the model and predicts the next of event. We have verified that GRTPP learns more flexible representation with relational information among events and produces more accurate predictions than state-of-art neural point process models. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Hawkes process▼atemporal point process▼adeep learning▼agraph neural network▼agraphical event modeling | - |
dc.subject | 호크과정▼a시간적 점과정▼a딥러닝▼a그래프 신경망▼a그래프 사건 모델 | - |
dc.title | Learning multivariate Hawkes process via graph neural network | - |
dc.title.alternative | 그래프 신경망을 이용한 다변량 호크 과정 학습 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :산업및시스템공학과, | - |
dc.contributor.alternativeauthor | 윤강훈 | - |
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