no code implementations • SMM4H (COLING) 2022 • Omar Adjali, Fréjus A. A. Laleye, Umang Aggarwal
We describe in this paper our proposed systems for the Social Media Mining for Health 2022 shared task 1.
no code implementations • 1 Feb 2022 • Umang Aggarwal, Adrian Popescu, Céline Hudelot
Here, we introduce a new active learning method which is designed for imbalanced datasets.
no code implementations • 1 Feb 2022 • Umang Aggarwal, Adrian Popescu, Eden Belouadah, Céline Hudelot
Since memory is bounded, old classes are learned with fewer images than new classes and an imbalance due to incremental learning is added to the initial dataset imbalance.
no code implementations • 18 Jan 2022 • Umang Aggarwal, Adrian Popescu, Céline Hudelot
It consists in learning a model on a small amount of annotated data (annotation budget) and in choosing the best set of points to annotate in order to improve the previous model and gain in generalization.
1 code implementation • 25 Aug 2020 • Eden Belouadah, Adrian Popescu, Umang Aggarwal, Léo Saci
Most existing algorithms make two strong hypotheses which reduce the realism of the incremental scenario: (1) new data are assumed to be readily annotated when streamed and (2) tests are run with balanced datasets while most real-life datasets are actually imbalanced.