4 citations · 7 across the 9 of their papers we have counts for
10 papers
Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning
Mindula Illeperuma, Rafael Pina, Charuka Herath +2
Structured pruning is a model compression technique that is used to reduce the computational cost of deploying deep neural networks on resource-constrained devices. Popular methods…
Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling
Rafael Pina, Varuna De Silva, Mindula Illeperuma
Machine learning can be crucial to help scale complex signal processing applications in scenarios such as healthcare. However, these machine learning models need rich datasets to b…
Robust and Efficient Communication for Multi-Agent Learning
Rafael Pina, Varuna De Silva, Corentin Artaud
Effective communication is a cornerstone of distributed intelligence in Multi-Agent Reinforcement Learning (MARL), yet ensuring that generated messages are both informative and rob…
Fully Independent Communication in Multi-Agent Reinforcement Learning
Rafael Pina, Varuna De Silva, Corentin Artaud +1
Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems. Several recent works have focused specifically on the study of…
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments
Rafael Pina, Corentin Artaud, Xiaolan Liu +1
Reinforcement Learning (RL) is an area of growing interest in the field of artificial intelligence due to its many notable applications in diverse fields. Particularly within the c…
Learning Independently from Causality in Multi-Agent Environments
Rafael Pina, Varuna De Silva, Corentin Artaud
Multi-Agent Reinforcement Learning (MARL) comprises an area of growing interest in the field of machine learning. Despite notable advances, there are still problems that require in…
Discovering Causality for Efficient Cooperation in Multi-Agent Environments
Rafael Pina, Varuna De Silva, Corentin Artaud
In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required to learn behaviours as a team to achieve a common goal. However, while learning a task, some agents may…
Causality Detection for Efficient Multi-Agent Reinforcement Learning
Rafael Pina, Varuna De Silva, Corentin Artaud
When learning a task as a team, some agents in Multi-Agent Reinforcement Learning (MARL) may fail to understand their true impact in the performance of the team. Such agents end up…
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football
Chaoyi Gu, Varuna De Silva, Corentin Artaud +1
Artificial Intelligence has been used to help human complete difficult tasks in complicated environments by providing optimized strategies for decision-making or replacing the manu…
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning
Rafael Pina, Varuna De Silva, Joosep Hook +1
Multi-Agent Reinforcement Learning (MARL) is useful in many problems that require the cooperation and coordination of multiple agents. Learning optimal policies using reinforcement…