213 citations · 629 across the 15 of their papers we have counts for
17 papers
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…
Evolving Multi-Label Fuzzy Classifier
Edwin Lughofer
Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time…
Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach
Mahardhika Pratama, Andri Ashfahani, Edwin Lughofer
Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground…
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams
Mahardhika Pratama, Choiru Za'in, Edwin Lughofer +2
The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imp…
Autonomous Deep Quality Monitoring in Streaming Environments
Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer +1
The common practice of quality monitoring in industry relies on manual inspection well-known to be slow, error-prone and operator-dependent. This issue raises strong demand for aut…