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Advantage Actor-Critic with Reasoner: Explaining the Agent's Behavior from an Exploratory Perspective
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.
Statistically Consistent Saliency Estimation
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.
Google Trends Analysis of COVID-19
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.
Addict Free -- A Smart and Connected Relapse Intervention Mobile App
It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods.
Discovering Opioid Use Patterns from Social Media for Relapse Prevention
The United States is currently experiencing an unprecedented opioid crisis, and opioid overdose has become a leading cause of injury and death.
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning
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.
Data Centers Job Scheduling with Deep Reinforcement Learning
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.
A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting
Precisely forecasting wind speed is essential for wind power producers and grid operators.
Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction
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.
Predicting Opioid Relapse Using Social Media Data
In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences.
Forecasting People's Needs in Hurricane Events from Social Network
Our proposed sequence to sequence method forecast people's needs more successfully than either of the other models.
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning
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
