4 citations · 7 across the 8 of their papers we have counts for
9 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…
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…
Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning
M. Anwar Ma'sum, Mahardhika Pratama, Igor Skrjanc
Data scarcity significantly complicates the continual learning problem, i.e., how a deep neural network learns in dynamic environments with very few samples. However, the latest pr…
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…
Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
Naeem Paeedeh, Mahardhika Pratama, Muhammad Anwar Ma'sum +3
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications. This paper proposes an adaptive trans…
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…
Assessor-Guided Learning for Continual Environments
Muhammad Anwar Ma'sum, Mahardhika Pratama, Edwin Lughofer +2
This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pac…