1 citations · 3 across the 13 of their papers we have counts for
14 papers
HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster
Mohamad A. Hady, Muhammad Anwar Masum, Siyi Hu +3
This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions including optical and Synthetic Aper…
KD-MARL: Resource-Aware Knowledge Distillation in Multi-Agent Reinforcement Learning
Monirul Islam Pavel, Siyi Hu, Muhammad Anwar Masum +3
Real world deployment of multi agent reinforcement learning MARL systems is fundamentally constrained by limited compute memory and inference time. While expert policies achieve hi…
Multi-Agent Reinforcement Learning for Heterogeneous Satellite Cluster Resources Optimization
Mohamad A. Hady, Siyi Hu, Mahardhika Pratama +2
This work investigates resource optimization in heterogeneous satellite clusters performing autonomous Earth Observation (EO) missions using Reinforcement Learning (RL). In the pro…
Continual Knowledge Consolidation LORA for Domain Incremental Learning
Naeem Paeedeh, Mahardhika Pratama, Weiping Ding +4
Domain Incremental Learning (DIL) is a sub-branch of continual learning that aims to address the never-ending arrival of new domains without catastrophic forgetting. Despite the ad…
PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning
M. Anwar Ma'sum, Mahardhika Pratama, Savitha Ramasamy +3
The data privacy constraint in online continual learning (OCL), where the data can be seen only once, complicates the catastrophic forgetting problem in streaming data. A common ap…
Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review
Siyi Hu, Mohamad A Hady, Jianglin Qiao +3
Multi-Agent Reinforcement Learning (MARL) has achieved strong performance in simulated benchmarks, yet real deployments often violate the assumptions under which algorithms are des…
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study
Mohamad A. Hady, Siyi Hu, Mahardhika Pratama +2
The exponential growth of Low Earth Orbit (LEO) satellites has revolutionised Earth Observation (EO) missions, addressing challenges in climate monitoring, disaster management, and…
Onboard Optimization and Learning: A Survey
Monirul Islam Pavel, Siyi Hu, Mahardhika Pratama +1
Onboard learning is a transformative approach in edge AI, enabling real-time data processing, decision-making, and adaptive model training directly on resource-constrained devices…
Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
Mohamad A. Hady, Siyi Hu, Mahardhika Pratama +2
Multi-Agent Reinforcement Learning (MARL) has become a powerful framework for numerous real-world applications, modeling distributed decision-making and learning from interactions…
PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning
Muhammad Anwar Ma'sum, Mahardhika Pratama, Savitha Ramasamy +3
Federated Class Incremental Learning (FCIL) is a new direction in continual learning (CL) for addressing catastrophic forgetting and non-IID data distribution simultaneously. Exist…
Unsupervised Few-Shot Continual Learning for Remote Sensing Image Scene Classification
Muhammad Anwar Ma'sum, Mahardhika Pratama, Ramasamy Savitha +3
A continual learning (CL) model is desired for remote sensing image analysis because of varying camera parameters, spectral ranges, resolutions, etc. There exist some recent initia…
Few-Shot Class Incremental Learning via Robust Transformer Approach
Naeem Paeedeh, Mahardhika Pratama, Sunu Wibirama +3
Few-Shot Class-Incremental Learning presents an extension of the Class Incremental Learning problem where a model is faced with the problem of data scarcity while addressing the ca…
Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks
Muhammad Anwar Ma'sum, MD Rasel Sarkar, Mahardhika Pratama +5
A reliable long-term time-series forecaster is highly demanded in practice but comes across many challenges such as low computational and memory footprints as well as robustness ag…
Few-Shot Continual Learning via Flat-to-Wide Approaches
Muhammad Anwar Ma'sum, Mahardhika Pratama, Edwin Lughofer +3
Existing approaches on continual learning call for a lot of samples in their training processes. Such approaches are impractical for many real-world problems having limited samples…