28 citations · 91 across the 19 of their papers we have counts for
21 papers
Structural Restricted Boltzmann Machine for image denoising and classification
Arkaitz Bidaurrazaga, Aritz Pérez, Roberto Santana
Restricted Boltzmann Machines are generative models that consist of a layer of hidden variables connected to another layer of visible units, and they are used to model the distribu…
Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification
Roberto Santana, Ivan Hidalgo-Cenalmor, Unai Garciarena +2
In some machine learning applications the availability of labeled instances for supervised classification is limited while unlabeled instances are abundant. Semi-supervised learnin…
On the Generalization of PINNs outside the training domain and the Hyperparameters influencing it
Andrea Bonfanti, Roberto Santana, Marco Ellero +1
Physics-Informed Neural Networks (PINNs) are Neural Network architectures trained to emulate solutions of differential equations without the necessity of solution data. They are cu…
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…
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
Jon Vadillo, Roberto Santana, Jose A. Lozano
Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models ca…
On the human evaluation of audio adversarial examples
Jon Vadillo, Roberto Santana
Human-machine interaction is increasingly dependent on speech communication. Machine Learning models are usually applied to interpret human speech commands. However, these models c…
Universal adversarial examples in speech command classification
Jon Vadillo, Roberto Santana
Adversarial examples are inputs intentionally perturbed with the aim of forcing a machine learning model to produce a wrong prediction, while the changes are not easily detectable…
Evolving Gaussian Process kernels from elementary mathematical expressions
Ibai Roman, Roberto Santana, Alexander Mendiburu +1
Choosing the most adequate kernel is crucial in many Machine Learning applications. Gaussian Process is a state-of-the-art technique for regression and classification that heavily…
Sentiment analysis with genetically evolved Gaussian kernels
Ibai Roman, Alexander Mendiburu, Roberto Santana +1
Sentiment analysis consists of evaluating opinions or statements from the analysis of text. Among the methods used to estimate the degree in which a text expresses a given sentimen…
Towards automatic construction of multi-network models for heterogeneous multi-task learning
Unai Garciarena, Alexander Mendiburu, Roberto Santana
Multi-task learning, as it is understood nowadays, consists of using one single model to carry out several similar tasks. From classifying hand-written characters of different alph…
On the performance of multi-objective estimation of distribution algorithms for combinatorial problems
Marcella S. R. Martins, Mohamed El Yafrani, Roberto Santana +3
Fitness landscape analysis investigates features with a high influence on the performance of optimization algorithms, aiming to take advantage of the addressed problem characterist…
Towards a more efficient representation of imputation operators in TPOT
Unai Garciarena, Alexander Mendiburu, Roberto Santana
Automated Machine Learning encompasses a set of meta-algorithms intended to design and apply machine learning techniques (e.g., model selection, hyperparameter tuning, model assess…
Gray-box optimization and factorized distribution algorithms: where two worlds collide
Roberto Santana
The concept of gray-box optimization, in juxtaposition to black-box optimization, revolves about the idea of exploiting the problem structure to implement more efficient evolutiona…
Evolving imputation strategies for missing data in classification problems with TPOT
Unai Garciarena, Roberto Santana, Alexander Mendiburu
Missing data has a ubiquitous presence in real-life applications of machine learning techniques. Imputation methods are algorithms conceived for restoring missing values in the dat…
Reproducing and learning new algebraic operations on word embeddings using genetic programming
Roberto Santana
Word-vector representations associate a high dimensional real-vector to every word from a corpus. Recently, neural-network based methods have been proposed for learning this repres…
Evolutionary Approaches to Optimization Problems in Chimera Topologies
Roberto Santana, Zheng Zhu, Helmut G. Katzgraber
Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate th…
Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions
Roberto Santana, Alexander Mendiburu, Jose A. Lozano
NM-landscapes have been recently introduced as a class of tunable rugged models. They are a subset of the general interaction models where all the interactions are of order less or…
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems
Murilo Zangari de Souza, Roberto Santana, Aurora Trinidad Ramirez Pozo +1
Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems. These algo…
A probabilistic evolutionary optimization approach to compute quasiparticle braids
Roberto Santana, Ross B. McDonald, Helmut G. Katzgraber
Topological quantum computing is an alternative framework for avoiding the quantum decoherence problem in quantum computation. The problem of executing a gate in this framework can…