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Found 7 papers, 0 papers with code
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.
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.
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.
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.
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 a more efficient representation of imputation operators in TPOT
We evaluate the method to introduce imputation methods as part of TPOT.
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.
