9 citations · 28 across the 22 of their papers we have counts for
4 papers · 1 filter
A Data-Centric Framework for Detecting and Correcting Corrupted Labels
Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +3
The performance of machine learning and deep learning models largely depends on the quality of the training data. However, the quality of the real-world datasets is often compromis…
Noise-Aware Framework for Correcting Corrupted Labels
Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +4
High-quality labeled data is essential for training reliable ML/DL models. However, real-world datasets often contain a considerable proportion of corrupted labels, which can sever…
Structured Exploration and Exploitation of Label Functions for Automated Data Annotation
Phong Lam, Ha-Linh Nguyen, Thu-Trang Nguyen +2
High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-prone. Programmatic labeling a…
iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
Dat Le, Duc-Cuong Le, Anh-Son Nguyen +4
Automated Machine Learning (AutoML) has improved access to machine learning, yet existing techniques often remain limited in flexibility, transparency, and execution reliability. C…