activity
20172021
most citedCOEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks

48 citations · 134 across the 9 of their papers we have counts for

collaborators

12 papers

q-bio.GN2021

Abordagem probabilística para análise de confiabilidade de dados gerados em sequenciamentos multiplex na plataforma ABI SOLiD

Fabio M. F. Lobato, Carlos D. N. Damasceno, Péricles L. Machado +6

The next-generation sequencers such as Illumina and SOLiD platforms generate a large amount of data, commonly above 10 Gigabytes of text files. Particularly, the SOLiD platform all…

cs.NE20211 cited

On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future

Unai Garciarena, Nuno Lourenço, Penousal Machado +2

Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures. In spite of this, due to t…

cs.NE202019 cited

Exploring the Evolution of GANs through Quality Diversity

Victor Costa, Nuno Lourenço, João Correia +1

Generative adversarial networks (GANs) achieved relevant advances in the field of generative algorithms, presenting high-quality results mainly in the context of images. However, G…

cs.NE20207 cited

AutoLR: An Evolutionary Approach to Learning Rate Policies

Pedro Carvalho, Nuno Lourenço, Filipe Assunção +1

The choice of a proper learning rate is paramount for good Artificial Neural Network training and performance. In the past, one had to rely on experience and trial-and-error to fin…

cs.NE201948 cited

COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks

Victor Costa, Nuno Lourenço, João Correia +1

Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to…

cs.NE201932 cited

Coevolution of Generative Adversarial Networks

Victor Costa, Nuno Lourenço, Penousal Machado

Generative adversarial networks (GAN) became a hot topic, presenting impressive results in the field of computer vision. However, there are still open problems with the GAN model,…