8 papers
Bridging Streaming Continual Learning via In-Context Large Tabular Models
Afonso Lourenço, João Gama, Eric P. Xing +1
In streaming scenarios, models must learn continuously, adapting to concept drifts without erasing previously acquired knowledge. However, existing research communities address the…
Explainable Anomaly Detection for Industrial IoT Data Streams
Ana Rita Paupério, Diogo Risca, Afonso Lourenço +2
Industrial maintenance is being transformed by the Internet of Things and edge computing, generating continuous data streams that demand real-time, adaptive decision-making under l…
Online hierarchical partitioning of the output space in extreme multi-label data stream
Lara Neves, Afonso Lourenço, Alberto Cano +1
Mining data streams with multi-label outputs poses significant challenges due to evolving distributions, high-dimensional label spaces, sparse label occurrences, and complex label…
Continual learning for rotating machinery fault diagnosis with cross-domain environmental and operational variations
Diogo Risca, Afonso Lourenço, Goreti Marreiros
Although numerous machine learning models exist to detect issues like rolling bearing strain and deformation, typically caused by improper mounting, overloading, or poor lubricatio…
Boosting-inspired online learning with transfer for railway maintenance
Diogo Risca, Afonso Lourenço, Goreti Marreiros
The integration of advanced sensor technologies with deep learning algorithms has revolutionized fault diagnosis in railway systems, particularly at the wheel-track interface. Alth…
On-device edge learning for IoT data streams: a survey
Afonso Lourenço, João Rodrigo, João Gama +1
This literature review explores continual learning methods for on-device training in the context of neural networks (NNs) and decision trees (DTs) for classification tasks on smart…