76 citations · 118 across the 14 of their papers we have counts for
8 papers · 1 filter
Controlled Dropout for Uncertainty Estimation
Mehedi Hasan, Abbas Khosravi, Ibrahim Hossain +2
Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art per…
Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning
Maryam Habibpour, Hassan Gharoun, Mohammadreza Mehdipour +7
Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwant…
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain +9
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of r…
Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework
Ngoc Duy Nguyen, Thanh Thi Nguyen, Hai Nguyen +2
The integration of deep learning to reinforcement learning (RL) has enabled RL to perform efficiently in high-dimensional environments. Deep RL methods have been applied to solve m…
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning
Ngoc Duy Nguyen, Thanh Thi Nguyen, Doug Creighton +1
Deep reinforcement learning has been applied successfully to solve various real-world problems and the number of its applications in the multi-agent settings has been increasing. M…
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great cha…