12 citations · 24 across the 16 of their papers we have counts for
17 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…
Beyond Peak TOPS/W: A System-Level Perspective on Hybrid Digital, Analogue and Neuromorphic Computing
Eiman Kanjo, Varuna De Silva
The digital revolution, which progressively replaced analogue methods with digital circuits, has entered a new phase as AI expands across cloud infrastructure, mobile networks, wea…
ATRACT: A Trustworthy Robotic Autonomous system to support Casualty Triage
Tasweer Ahmad, Rafael Pina, Sandip Pradhan +6
At a time when drones are increasingly associated with hostile operations, we re-purpose them for humanitarian and life-saving applications. However, adapting search and rescue dro…
Player Pressure Map -- A Novel Representation of Pressure in Soccer for Evaluating Player Performance in Different Game Contexts
Chaoyi Gu, Jiaming Na, Yisheng Pei +1
In soccer, contextual player performance metrics are invaluable to coaches. For example, the ability to perform under pressure during matches distinguishes the elite from the avera…
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…
Passing Heatmap Prediction Based on Transformer Model and Tracking Data
Yisheng Pei, Varuna De Silva, Mike Caine
Although the data-driven analysis of football players' performance has been developed for years, most research only focuses on the on-ball event including shots and passes, while t…
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…
Deep Generative Multi-Agent Imitation Model as a Computational Benchmark for Evaluating Human Performance in Complex Interactive Tasks: A Case Study in Football
Chaoyi Gu, Varuna De Silva
Evaluating the performance of human is a common need across many applications, such as in engineering and sports. When evaluating human performance in completing complex and intera…
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…
Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks
Haileleol Tibebu, Aadil Malik, Varuna De Silva
Synthesizing a realistic image from textual description is a major challenge in computer vision. Current text to image synthesis approaches falls short of producing a highresolutio…
Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots
Varuna De Silva, Jamie Roche, Ahmet Kondoz
Autonomous robots that assist humans in day to day living tasks are becoming increasingly popular. Autonomous mobile robots operate by sensing and perceiving their surrounding envi…
An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles
Varuna De Silva, Xiongzhao Wang, Deniz Aladagli +2
Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence,…