18 citations · 37 across the 6 of their papers we have counts for
6 papers
Adversarial Attacks Assessment of Salient Object Detection via Symbolic Learning
Gustavo Olague, Roberto Pineda, Gerardo Ibarra-Vazquez +5
Machine learning is at the center of mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial featur…
Automated Design of Salient Object Detection Algorithms with Brain Programming
Gustavo Olague, Jose Armando Menendez-Clavijo, Matthieu Olague +4
Despite recent improvements in computer vision, artificial visual systems' design is still daunting since an explanation of visual computing algorithms remains elusive. Salient obj…
Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning
Gerardo Ibarra-Vazquez, Gustavo Olague, Mariana Chan-Ley +2
In recent years, the security concerns about the vulnerability of Deep Convolutional Neural Networks (DCNN) to Adversarial Attacks (AA) in the form of small modifications to the in…
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations
Gustavo Olague, Gerardo Ibarra-Vazquez, Mariana Chan-Ley +3
Art Media Classification problem is a current research area that has attracted attention due to the complex extraction and analysis of features of high-value art pieces. The percep…
It is Time for New Perspectives on How to Fight Bloat in GP
Francisco Fernández de Vega, Gustavo Olague, Francisco Chávez +3
The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches domin…
Increasing GP Computing Power via Volunteer Computing
Daniel Lombrana Gonzalez, Francisco Fernandez de Vega, L. Trujillo +6
This paper describes how it is possible to increase GP Computing Power via Volunteer Computing (VC) using the BOINC framework. Two experiments using well-known GP tools -Lil-gp & E…