5 citations · 5 across the 3 of their papers we have counts for
5 papers
Physical knowledge on historical data matters more than enforcing physical constraints on the forecast
Etienne Lehembre, Pascal Audigane, Vincent Nguyen +2
Time series forecasting has seen signicant advancements with the emergence of new deep learning models. However, forecasting time series in applications involving physical processe…
GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
Trung-Anh Dang, Duy-Cuong Bui, Ngoc-Son Vu +2
Continual learning faces the persistent challenge of catastrophic forgetting, where sequential task updates degrade previously acquired knowledge. While prompt-based methods integr…
SAMix: Calibrated and Accurate Continual Learning via Sphere-Adaptive Mixup and Neural Collapse
Trung-Anh Dang, Vincent Nguyen, Ngoc-Son Vu +1
While most continual learning methods focus on mitigating forgetting and improving accuracy, they often overlook the critical aspect of network calibration, despite its importance.…
Memory-efficient Continual Learning with Neural Collapse Contrastive
Trung-Anh Dang, Vincent Nguyen, Ngoc-Son Vu +1
Contrastive learning has significantly improved representation quality, enhancing knowledge transfer across tasks in continual learning (CL). However, catastrophic forgetting remai…
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Zied Bouraoui, Antoine Cornuéjols, Thierry Denœux +9
This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been deve…