Daniel Molina | DeepAI

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∙ 07/26/2023

General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Open Challenges and Implications

Most applications of Artificial Intelligence (AI) are designed for a con...

0 Isaac Triguero, et al. ∙

∙ 02/20/2023

Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness

Evolutionary Computation algorithms have been used to solve optimization...

0 Javier Poyatos, et al. ∙

∙ 02/08/2022

EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks

In recent years, Deep Learning models have shown a great performance in ...

0 Javier Poyatos, et al. ∙

∙ 08/09/2020

Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges

Much has been said about the fusion of bio-inspired optimization algorit...

0 Aritz D. Martinez, et al. ∙

∙ 04/19/2020

Fairness in Bio-inspired Optimization Research: A Prescription of Methodological Guidelines for Comparing Meta-heuristics

Bio-inspired optimization (including Evolutionary Computation and Swarm ...

0 Antonio LaTorre, et al. ∙

∙ 02/19/2020

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms ...

0 Daniel Molina, et al. ∙

∙ 02/19/2020

Taxonomy of bio-inspired algorithms

In recent years, a great variety of nature and bio-inspired algorithms h...

0 Daniel Molina, et al. ∙

∙ 10/22/2019

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

In the last years, Artificial Intelligence (AI) has achieved a notable m...

170 Alejandro Barredo Arrieta, et al. ∙

∙ 03/08/2019

Identifying Critical Regions for Motion Planning using Auto-Generated Saliency Labels with Convolutional Neural Networks

In this paper, we present a new approach to learning for motion planning...

0 Daniel Molina, et al. ∙

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