353 citations
- Université de MontréalCA22 papers
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- Université du Québec à MontréalCA13 papers
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- Helsinki Institute of PhysicsFI11 papers
- Polytechnique MontréalCA11 papers
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14 papers · 1 filter
Blockchain-based Crowdsourced Deep Reinforcement Learning as a Service
Ahmed Alagha, Hadi Otrok, Shakti Singh +2
Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its…
Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning
Ahmed Alagha, Jamal Bentahar, Hadi Otrok +2
Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes…
Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer
Ahmed Alagha, Rabeb Mizouni, Shakti Singh +2
Target localization is a critical task in sensitive applications, where multiple sensing agents communicate and collaborate to identify the target location based on sensor readings…
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder
Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are mad…
on the effectiveness of generative adversarial network on anomaly detection
Laya Rafiee Sevyeri, Thomas Fevens
Identifying anomalies refers to detecting samples that do not resemble the training data distribution. Many generative models have been used to find anomalies, and among them, gene…
Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories
Ali Yazdizadeh, Arash Kalatian, Zachary Patterson +1
Multi-task learning is assumed as a powerful inference method, specifically, where there is a considerable correlation between multiple tasks, predicting them in an unique framewor…