Javier Del Ser

research

∙ 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. ∙

research

∙ 05/24/2023

ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification

In the era of sustainable smart agriculture, a massive amount of agricul...

0 Biao Zhao, et al. ∙

research

∙ 05/02/2023

Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation

Trustworthy Artificial Intelligence (AI) is based on seven technical req...

0 Natalia Díaz Rodríguez, et al. ∙

research

∙ 04/18/2023

Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments

One of the key challenges of Reinforcement Learning (RL) is the ability ...

0 Alain Andres, et al. ∙

research

∙ 03/14/2023

On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence

AI-based digital twins are at the leading edge of the Industry 4.0 revol...

0 Jesus L. Lobo, et al. ∙

research

∙ 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. ∙

research

∙ 12/07/2022

Deep Learning for Brain Age Estimation: A Systematic Review

Over the years, Machine Learning models have been successfully employed ...

0 M. Tanveer, et al. ∙

research

∙ 11/30/2022

Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation

Reinforcement Learning has emerged as a strong alternative to solve opti...

0 Alain Andres, et al. ∙

research

∙ 10/28/2022

Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability

The estimation of the amount of uncertainty featured by predictive machi...

0 Ibai Lana, et al. ∙

research

∙ 09/30/2022

A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks

Research around Spiking Neural Networks has ignited during the last year...

0 Aitor Martinez Seras, et al. ∙

research

∙ 09/29/2022

Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques

In this paper three customised Artificial Intelligence (AI) frameworks, ...

0 Dušan Fister, et al. ∙

research

∙ 09/26/2022

Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification

Although Deep Neural Networks (DNNs) have great generalization and predi...

19 Adrien Bennetot, et al. ∙

research

∙ 09/17/2022

Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey

Data quality is the key factor for the development of trustworthy AI in ...

0 Xiaodan Xing, et al. ∙

research

∙ 08/11/2022

Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation

Despite recent advances in the accuracy of brain tumor segmentation, the...

17 Hao Li, et al. ∙

research

∙ 07/28/2022

Deep learning for understanding multilabel imbalanced Chest X-ray datasets

Over the last few years, convolutional neural networks (CNNs) have domin...

18 Helena Liz, et al. ∙

research

∙ 07/19/2022

Large-Kernel Attention for 3D Medical Image Segmentation

Automatic segmentation of multiple organs and tumors from 3D medical ima...

12 Hao Li, et al. ∙

research

∙ 07/18/2022

Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Ring Design Generation

Rendering programs have changed the design process completely as they pe...

0 Tomas Cabezon Pedroso, et al. ∙

research

∙ 06/03/2022

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Atmospheric Extreme Events (EEs) cause severe damages to human societies...

0 Sancho Salcedo-Sanz, et al. ∙

research

∙ 05/23/2022

An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments

In the last few years, the research activity around reinforcement learni...

0 Alain Andres, et al. ∙

research

∙ 05/20/2022

Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization

There is a broad consensus on the importance of deep learning models in ...

0 Javier Del Ser, et al. ∙

research

∙ 02/24/2022

Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?

In the early stages of human life, babies develop their skills by explor...

0 Alain Andres, et al. ∙

research

∙ 02/11/2022

Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels

The upheaval brought by the arrival of the COVID-19 pandemic has continu...

6 Ming Li, et al. ∙

research

∙ 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. ∙

research

∙ 01/31/2022

AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus

Based on CT and MRI images acquired from normal pressure hydrocephalus (...

0 Xi Zhou, et al. ∙

research

∙ 01/17/2022

Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions

Removing the bias and variance of multicentre data has always been a cha...

26 Yang Nan, et al. ∙

research

∙ 01/11/2022

On the Design of Graph Embeddings for the Sensorless Estimation of Road Traffic Profiles

Traffic forecasting models rely on data that needs to be sensed, process...

4 Eric L. Manibardo, et al. ∙

research

∙ 12/10/2021

Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information

In clinical medicine, magnetic resonance imaging (MRI) is one of the mos...

0 Jiahao Huang, et al. ∙

research

∙ 11/29/2021

Evolutionary Multitask Optimization: Are we Moving in the Right Direction?

Transfer Optimization, understood as the exchange of information among s...

0 Eneko Osaba, et al. ∙

research

∙ 04/02/2021

Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

Most of state of the art methods applied on time series consist of deep ...

0 Thomas Rojat, et al. ∙

research

∙ 03/28/2021

A survey on extremism analysis using Natural Language Processing

Extremism research has grown as an open problem for several countries du...

0 Javier Torregrosa, et al. ∙

research

∙ 03/26/2021

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Randomization-based Machine Learning methods for prediction are currentl...

0 Javier Del Ser, et al. ∙

research

∙ 02/17/2021

On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification

Since their inception, learning techniques under the Reservoir Computing...

0 Alejandro Barredo Arrieta, et al. ∙

research

∙ 02/04/2021

Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

In this work we consider multitasking in the context of solving multiple...

50 Eneko Osaba, et al. ∙

research

∙ 12/02/2020

Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

Deep Learning methods have been proven to be flexible to model complex p...

0 Eric L. Manibardo, et al. ∙

research

∙ 10/08/2020

AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

Transfer Optimization is an incipient research area dedicated to the sim...

0 Eneko Osaba, et al. ∙

research

∙ 09/30/2020

A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS): Application to Community Detection over Graphs

The main goal of the multitasking optimization paradigm is to solve mult...

0 Eneko Osaba, et al. ∙

research

∙ 09/21/2020

CURIE: A Cellular Automaton for Concept Drift Detection

Data stream mining extracts information from large quantities of data fl...

0 Jesus L. Lobo, et al. ∙

research

∙ 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. ∙

research

∙ 05/11/2020

On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking

Multitasking optimization is a recently introduced paradigm, focused on ...

0 Eneko Osaba, et al. ∙

research

∙ 05/11/2020

On the Transferability of Knowledge among Vehicle Routing Problems by using a Cellular Evolutionary Multitasking

Multitasking optimization is a recently introduced paradigm, focused on ...

0 Eneko Osaba, et al. ∙

research

∙ 05/08/2020

Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls

This work aims at unveiling the potential of Transfer Learning (TL) for ...

0 Eric L. Manibardo, et al. ∙

research

∙ 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. ∙

research

∙ 04/17/2020

Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment

In short-term traffic forecasting, the goal is to accurately predict fut...

0 Javier Del Ser, et al. ∙

research

∙ 04/14/2020

dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems

The emerging research paradigm coined as multitasking optimization aims ...

0 Eneko Osaba, et al. ∙

research

∙ 03/27/2020

New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data

This work focuses on classification over time series data. When a time s...

0 Eric L. Manibardo, et al. ∙

research

∙ 03/25/2020

Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples

The last decade has witnessed the proliferation of Deep Learning models ...

21 Alejandro Barredo Arrieta, et al. ∙

research

∙ 03/24/2020

COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking

Multitasking optimization is an emerging research field which has attrac...

0 Eneko Osaba, et al. ∙

research

∙ 03/24/2020

Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis

Multitasking optimization is an incipient research area which is lately ...

0 Eneko Osaba, et al. ∙

research

∙ 02/25/2020

Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization

In recent years, Multifactorial Optimization (MFO) has gained a notable ...

0 Aritz D. Martinez, et al. ∙

research

∙ 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. ∙

Javier Del Ser

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