Maya Okawa
Nippon Telegraph and Telephone
21 Papers
64 Citations
Maya Okawa is an academic researcher from Nippon Telegraph and Telephone. The author has contributed to research in topics: Computer science & Gaussian process. The author has an hindex of 6, co-authored 17 publications. Previous affiliations of Maya Okawa include Kyoto University.
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Papers
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information.
TL;DR: This paper proposes DMPP (Deep Mixture Point Processes), a point process model for predicting spatio-temporal events with the use of rich contextual information; a key advance is its incorporation of the heterogeneous and high-dimensional context available in image and text data.
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Maya Okawa,Tomoharu Iwata,Takeshi Kurashima,Yusuke Tanaka,Hiroyuki Toda,Naonori Ueda +5 more
- 25 Jul 2019
TL;DR: In this paper, a point process model for predicting spatio-temporal events with the use of rich contextual information is proposed, which incorporates the heterogeneous and high-dimensional context available in image and text data.
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•Proceedings Article
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
Yusuke Tanaka,Toshiyuki Tanaka,Tomoharu Iwata,Takeshi Kurashima,Maya Okawa,Yasunori Akagi,Hiroyuki Toda +6 more
- 01 Jan 2019
TL;DR: A probabilistic model for inferring the multivariate function from multiple areal data sets with various granularities that can accurately refine coarse-grainedAreal data, and offer performance improvements by using the arealData sets from multiple domains.
Real-time and proactive navigation via spatio-temporal prediction
Naonori Ueda,Futoshi Naya,Hitoshi Shimizu,Tomoharu Iwata,Maya Okawa,Hiroshi Sawada +5 more
- 07 Sep 2015
TL;DR: This work tries to detect future congestion by using a spatio-temporal statistical method that predicts people flow and creates an optimal navigation plan based on "what-if" simulations, which accounts for the effect of total people flow change caused by navigation.
13
•Posted Content
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
TL;DR: In this article, a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets is proposed, which can effectively make use of auxiliary data sets with various granularities.
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