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Found 24 papers, 4 papers with code
A probabilistic evolutionary optimization approach to compute quasiparticle braids
Topological quantum computing is an alternative framework for avoiding the quantum decoherence problem in quantum computation.
MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems
Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems.
Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions
NM-landscapes have been recently introduced as a class of tunable rugged models.
Evolutionary Approaches to Optimization Problems in Chimera Topologies
In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology.
Reproducing and learning new algebraic operations on word embeddings using genetic programming
We show that our method is able to reproduce the same behavior as human-designed algebraic operators.
Evolving imputation strategies for missing data in classification problems with TPOT
Missing data has a ubiquitous presence in real-life applications of machine learning techniques.
Gray-box optimization and factorized distribution algorithms: where two worlds collide
To illustrate this claim, we present a contrasted analysis of formalisms, questions, and results produced in FDAs and gray-box optimization.
Towards a more efficient representation of imputation operators in TPOT
We evaluate the method to introduce imputation methods as part of TPOT.
Expanding variational autoencoders for learning and exploiting latent representations in search distributions
In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best solutions incorporated latent or hidden vari- ables to the models as a more accurate way to represent the search distributions.
Towards automatic construction of multi-network models for heterogeneous multi-task learning
Multi-task learning, as it is understood nowadays, consists of using one single model to carry out several similar tasks.
Sentiment analysis with genetically evolved Gaussian kernels
Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.
Evolving Gaussian Process kernels from elementary mathematical expressions
Gaussian Process is a state-of-the-art technique for regression and classification that heavily relies on a kernel function.
Universal adversarial examples in speech command classification
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 by a human.
On the human evaluation of audio adversarial examples
In this paper we investigate to which extent the distortion metrics proposed in the literature for audio adversarial examples, and which are commonly applied to evaluate the effectiveness of methods for generating these attacks, are a reliable measure of the human perception of the perturbations.
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
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 can be easily fooled by the addition of imperceptible yet malicious perturbations to natural inputs.
Analysis of Dominant Classes in Universal Adversarial Perturbations
The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion.
On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future
Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures.
Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components
In this paper, we investigate the effect of different variation operators in a complex domain, that of multi-network heterogeneous neural models.
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations.
On the Generalization of PINNs outside the training domain and the Hyperparameters influencing it
Physics-Informed Neural Networks (PINNs) are Neural Network architectures trained to emulate solutions of differential equations without the necessity of solution data.
Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification
We assess the impact of these functions on semi-supervised problems with a varying amount of labeled instances.
Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approach
In scheduling problems common in the industry and various real-world scenarios, responding in real-time to disruptive events is essential.
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real-time solutions.
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications.
