Robust Collaborative Learning for Sequence Modelling

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.