Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Menghai Pan"'
Publikováno v:
Graph Neural Networks: Foundations, Frontiers, and Applications ISBN: 9789811660535
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f6c7631077e8bc22fc0cd1f248e734d8
https://doi.org/10.1007/978-981-16-6054-2_27
https://doi.org/10.1007/978-981-16-6054-2_27
Autor:
Huiyuan Chen, Yusan Lin, Menghai Pan, Lan Wang, Chin-Chia Michael Yeh, Xiaoting Li, Yan Zheng, Fei Wang, Hao Yang
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies. This is mainly attributed to their unique self-attention networks to exploit pairwise item-item interactions within
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a83259046a583432219a5da20646ed08
Autor:
Rui Song, Hui Lu, Yanhua Li, Zhihong Tian, Weixiao Huang, Menghai Pan, Zhenming Liu, Jun Luo, Xun Zhou
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 11:1-19
Many real-world human behaviors can be modeled and characterized as sequential decision-making processes, such as a taxi driver’s choices of working regions and times. Each driver possesses unique preferences on the sequential choices over time and
Publikováno v:
SIGSPATIAL/GIS
Thanks to the rapid development of mobile sensing techniques, massive human-generated spatial-temporal data (HSTD) are generated from the urban areas, e.g., passenger-seeking trajectories from taxi drivers, and public transit trips from urban dweller
Publikováno v:
SIGSPATIAL/GIS
Learning to make optimal decisions is a common yet complicated task. While computer agents can learn to make decisions by running reinforcement learning (RL), it remains unclear how human beings learn. In this paper, we perform the first data-driven
xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis
Publikováno v:
KDD
To make daily decisions, human agents devise their own "strategies" governing their mobility dynamics (e.g., taxi drivers have preferred working regions and times, and urban commuters have preferred routes and transit modes). Recent research such as
Publikováno v:
KDD
Given the historical movement trajectories of a set of individual human agents (e.g., pedestrians, taxi drivers) and a set of new trajectories claimed to be generated by a specific agent, the Human Mobility Signature Identification (HuMID) problem ai
Publikováno v:
INFOCOM Workshops
Rapid pace of global urbanization has posed significant challenges to urban transportation infrastructures. Existing urban transit systems suffer many well-known shortcomings, where public transits have limits on coverage areas, and fixed schedules,