no code implementations • 9 Sep 2023 • Muzhe Guo, Feixu Yu, Tian Lan, Fang Jin
Reinforcement learning (RL) is a powerful tool for solving complex decision-making problems, but its lack of transparency and interpretability has been a major challenge in domains where decisions have significant real-world consequences.
no code implementations • ICCV 2021 • Shunyan Luo, Emre Barut, Fang Jin
The growing use of deep learning for a wide range of data problems has highlighted the need to understand and diagnose these models appropriately, making deep learning interpretation techniques an essential tool for data analysts.
no code implementations • 7 Nov 2020 • Hoang Long Nguyen, Zhenhe Pan, Hashim Abu-gellban, Fang Jin, Yuanlin Zhang
Our results show that Google search trends are highly associated with the number of reported confirmed cases, where the Deep Learning approach outperforms other forecasting techniques.
no code implementations • 2 Dec 2019 • Zhou Yang, Vinay Jayachandra Reddy, Rashmi Kesidi, Fang Jin
It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods.
no code implementations • 2 Dec 2019 • Zhou Yang, Spencer Bradshaw, Rattikorn Hewett, Fang Jin
The United States is currently experiencing an unprecedented opioid crisis, and opioid overdose has become a leading cause of injury and death.
no code implementations • 17 Sep 2019 • Long H. Nguyen, Zhenhe Pan, Opeyemi Openiyi, Hashim Abu-gellban, Mahdi Moghadasi, Fang Jin
A robust model for time series forecasting is highly important in many domains, including but not limited to financial forecast, air temperature and electricity consumption.
no code implementations • 16 Sep 2019 • Sisheng Liang, Zhou Yang, Fang Jin, Yong Chen
Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space.
no code implementations • 24 Nov 2018 • Sisheng Liang, Long Nguyen, Fang Jin
Precisely forecasting wind speed is essential for wind power producers and grid operators.
no code implementations • 16 Nov 2018 • Long Nguyen, Jia Zhen, Zhe Lin, Hanxiang Du, Zhou Yang, Wenxuan Guo, Fang Jin
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production.
no code implementations • 14 Nov 2018 • Zhou Yang, Long Nguyen, Fang Jin
In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences.
no code implementations • 12 Nov 2018 • Long Nguyen, Zhou Yang, Jia Li, Guofeng Cao, Fang Jin
Our proposed sequence to sequence method forecast people's needs more successfully than either of the other models.
no code implementations • 12 Nov 2018 • Long Nguyen, Zhou Yang, Jiazhen Zhu, Jia Li, Fang Jin
To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule the rapid deployment of volunteers to rescue victims in dynamic settings.
Multi-agent Reinforcement Learning
reinforcement-learning
+2