Robust Collaborative Learning for Sequence Modelling

Francois Buet-Golfouse; Hans Roggeman; Islam Utyagulov

ICASSP 2022accepted0 citations

Robust Collaborative Learning for Sequence Modelling

Francois Buet-Golfouse, Hans Roggeman, Islam Utyagulov

Abstract

Current deep learning techniques for RNA classification suffer from over-fitting and lack of reproducibility. We show that by introducing robustness by design in both CNN and RNN algorithms, we are able to achieve standalone state-of-the-art accuracy. By constructing model-agnostic robustness checks and reusing features obtained from both architectures, we build a collaborative framework that improves performance and stability.

BibTeX

@inproceedings{icassp2022_robustcollaborat,
  title = {Robust Collaborative Learning for Sequence Modelling},
  author = {Francois Buet-Golfouse and Hans Roggeman and Islam Utyagulov},
  booktitle = {ICASSP 2022},
  year = {2022}
}