1 citations · 6 across the 22 of their papers we have counts for
13 papers · 1 filter
Source-Free Cross-Domain Continual Learning
Muhammad Tanzil Furqon, Mahardhika Pratama, Igor Škrjanc +3
Although existing cross-domain continual learning approaches successfully address many streaming tasks having domain shifts, they call for a fully labeled source domain hindering t…
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…
Black-Box Time-Series Domain Adaptation via Cross-Prompt Foundation Models
M. T. Furqon, Mahardhika Pratama, Igor Skrjanc +3
The black-box domain adaptation (BBDA) topic is developed to address the privacy and security issues where only an application programming interface (API) of the source model is av…
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…
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…
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…
Time and Frequency Synergy for Source-Free Time-Series Domain Adaptations
Muhammad Tanzil Furqon, Mahardhika Pratama, Ary Mazharuddin Shiddiqi +3
The issue of source-free time-series domain adaptations still gains scarce research attentions. On the other hand, existing approaches rely solely on time-domain features ignoring…
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…
Graph Mining under Data scarcity
Appan Rakaraddi, Lam Siew-Kei, Mahardhika Pratama +1
Multitude of deep learning models have been proposed for node classification in graphs. However, they tend to perform poorly under labeled-data scarcity. Although Few-shot learning…
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…
Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions
Muhammad Tanzil Furqon, Mahardhika Pratama, Lin Liu +2
Remaining Useful Life (RUL) predictions play vital role for asset planning and maintenance leading to many benefits to industries such as reduced downtime, low maintenance costs, e…
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…