{"type":"ScholarlyArticle","title":"Context Aware Sequence Modeling","authors":[{"name":"Kyungwoo Song","slug":"kyungwoo-song","given":"Kyungwoo","family":"Song","affiliation":"KAIST , South Korea"}],"abstract":"Context modeling helps understand the data, such as sentence or user behavior. Contextual information captures the important underlying feature, and it enhances the relationship between data instances or hidden representations. As the importance of the sequential model grows, so does the importance of the sequential contextual modeling. Under the sequential data, we need to consider the context change over time. In this paper, we present our research works on context modeling and its dynamics modeling over time. Furthermore, we extend our research to handle the multi-granularity of sequential context modeling to consider rich context representations.","doi":"10.24963/ijcai.2020/744","url":"https://ijcai.org/proceedings/2020/744","pdf":"https://ijcai.org/proceedings/2020/0744.pdf","markdown":"https://ijcai.org/proceedings/2020/744.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/744","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":744,"pages":{"first":5208,"last":5209},"keywords":["Machine Learning: Deep Learning","Machine Learning: Deep Learning: Sequence Modeling","Data Mining: Mining Text, Web, Social Media","Machine Learning: Probabilistic Machine Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25478/file_25478.m3u8","length_sec":280,"provider":"hls"}],"references":[{"position":1,"raw":"[Blei et al., 2003] David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993-1022, 2003.","title":"Latent dirichlet allocation","authors":"Andrew Y David M Blei, Michael I Ng, Jordan","year":"2003","venue":"Journal of machine Learning research","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Gerrish and Blei, 2012] Sean Gerrish and David M Blei. How they vote: Issue-adjusted models of legislative be- havior. In Advances in Neural Information Processing Sys- tems, pages 2753-2761, 2012.","title":"How they vote: Issue-adjusted models of legislative behavior","authors":"Blei Gerrish, Sean Gerrish, David M Blei","year":"2012","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Song et al., 2018] Kyungwoo Song, Wonsung Lee, and Il- Chul Moon. Neural ideal point estimation network. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.","title":"Kyungwoo Song, Wonsung Lee, and Il-Chul Moon. Neural ideal point estimation network","authors":"Song","year":"2018","venue":"Thirty-Second AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Song et al., 2019] Kyungwoo Song, Mingi Ji, Sungrae Park, and Il-Chul Moon. Hierarchical context enabled recurrent neural network for recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4983-4991, 2019.","title":"Kyungwoo Song, Mingi Ji, Sungrae Park, and Il-Chul Moon. Hierarchical context enabled recurrent neural network for recommendation","authors":"Song","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Song et al., 2020] Kyungwoo Song, JoonHo Jang, Il-Chul Moon, et al. Bivariate beta lstm. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 2020.","title":"Kyungwoo Song, JoonHo Jang, Il-Chul Moon, et al. Bivariate beta lstm","authors":"Song","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Williams and Rasmussen, 2006] Christopher KI Williams and Carl Edward Rasmussen. Gaussian processes for ma- chine learning, volume 2. MIT press Cambridge, MA, 2006.","title":"Gaussian processes for machine learning","authors":"K I Christopher, Carl Edward Williams, Rasmussen","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}