Code
01D31417
Duration
01 October 2017 → 30 September 2018
Funding
Regional and community funding: Special Research Fund
Promotor
Fellow
Research disciplines
-
Natural sciences
- Bioinformatics and computational biology not elsewhere classified
Keywords
recurrent neural networks
machine learning
deep learning
convolutional neural networks
robustness
Project description
Deep neural networks appear to be very sensitive to small perturbations to their input that should leave their output unchanged. However, specially crafted tiny perturbations can change the output of these networks dramatically, a phenomenon which is not yet well understood. In this work, we will aim to find a formal characterization of these perturbations.