28 citations · 91 across the 19 of their papers we have counts for
3 papers · 1 filter
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Jon Vadillo, Roberto Santana, Jose A. Lozano
Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpr…
Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components
Unai Garciarena, Roberto Santana, Alexander Mendiburu
With neural architecture search methods gaining ground on manually designed deep neural networks -even more rapidly as model sophistication escalates-, the research trend shifts to…
Analysis of Dominant Classes in Universal Adversarial Perturbations
Jon Vadillo, Roberto Santana, Jose A. Lozano
The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many different strategies can be employed to effici…