Zobrazeno 1 - 10
of 214
pro vyhledávání: '"Fukushima, Shintaro"'
Large Language Models are applied to recommendation tasks such as items to buy and news articles to read. Point of Interest is quite a new area to sequential recommendation based on language representations of multimodal datasets. As a first step to
Externí odkaz:
http://arxiv.org/abs/2410.03265
Autor:
Fukushima, Shintaro, Yamanishi, Kenji
This study addresses the issue of balancing graph summarization and graph change detection. Graph summarization compresses large-scale graphs into a smaller scale. However, the question remains: To what extent should the original graph be compressed?
Externí odkaz:
http://arxiv.org/abs/2311.18694
Autor:
Xu, Xiaohang, Suzumura, Toyotaro, Yong, Jiawei, Hanai, Masatoshi, Yang, Chuang, Kanezashi, Hiroki, Jiang, Renhe, Fukushima, Shintaro
Next Point-of-Interest (POI) recommendation plays a crucial role in urban mobility applications. Recently, POI recommendation models based on Graph Neural Networks (GNN) have been extensively studied and achieved, however, the effective incorporation
Externí odkaz:
http://arxiv.org/abs/2310.01224
Autor:
Jiang, Renhe, Wang, Zhaonan, Yong, Jiawei, Jeph, Puneet, Chen, Quanjun, Kobayashi, Yasumasa, Song, Xuan, Suzumura, Toyotaro, Fukushima, Shintaro
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the graph streams, in this study, we p
Externí odkaz:
http://arxiv.org/abs/2212.05989
Autor:
Jiang, Renhe, Wang, Zhaonan, Yong, Jiawei, Jeph, Puneet, Chen, Quanjun, Kobayashi, Yasumasa, Song, Xuan, Fukushima, Shintaro, Suzumura, Toyotaro
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we
Externí odkaz:
http://arxiv.org/abs/2211.14701
Autor:
Koga, Chie, Takemura, Kosuke, Shin, Yuta, Fukushima, Shintaro, Uchida, Yukiko, Yoshimura, Yuji
Publikováno v:
In Cities November 2024 154
Publikováno v:
In Current Opinion in Psychology February 2024 55
Autor:
Fukushima, Shintaro, Yamanishi, Kenji
This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams. There are three different levels of changes for latent variable models: 1) the first level is the change in data distribution for fix
Externí odkaz:
http://arxiv.org/abs/2011.09465
We are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warning signals of outbreaks by detecting signs of changes in the cases. We
Externí odkaz:
http://arxiv.org/abs/2007.15179
In online learning from non-stationary data streams, it is necessary to learn robustly to outliers and to adapt quickly to changes in the underlying data generating mechanism. In this paper, we refer to the former attribute of online learning algorit
Externí odkaz:
http://arxiv.org/abs/2007.12160