Assistant Professor
Email: alcolea@unizar.es
Address: Campus Río Ebro, University of Zaragoza
C/María de Luna 1, Ada Byron Building,
50018, Zaragoza, Spain
ABOUT ME
I am an assistant professor in the Computer Architecture Group (gaZ) at the University of Zaragoza.
Before that, I graduated in Social Work from the University of Zaragoza, Spain (2010), I obtained my master degree in International Peace, Conflict and Development Studies from the University Jaume I of Castellón, Spain (2012), I graduated in Informatics Engineering from the University of Zaragoza, Spain (2017), and then I received my PhD in System Engineering and Computer Science at the University of Zaragoza, Spain ().
Current research
My main research topic is the search for efficient and reliable machine learning algorithms, which includes the analysis and improvement of models (especially Bayesian models) and the development of hardware support for them.
PUBLICATIONS
2022
Journal Articles
Alcolea, Adrián; Resano, Javier
Bayesian Neural Networks to Analyze Hyperspectral Datasets Using Uncertainty Metrics Journal Article
In: IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-10, 2022, ISSN: 1558-0644.
@article{9881579,
title = {Bayesian Neural Networks to Analyze Hyperspectral Datasets Using Uncertainty Metrics},
author = {Adrián Alcolea and Javier Resano},
doi = {10.1109/TGRS.2022.3205119},
issn = {1558-0644},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {60},
pages = {1-10},
abstract = {Machine learning techniques, and specifically neural networks, have proved to be very useful tools for image classification tasks. Nevertheless, measuring the reliability of these networks and calibrating them accurately are very complex. This is even more complex in a field like hyperspectral imaging, where labeled data are scarce and difficult to generate. Bayesian neural networks (BNNs) allow to obtain uncertainty metrics related to the data processed (aleatoric), and to the uncertainty generated by the model selected (epistemic). On this work, we will demonstrate the utility of BNNs by analyzing the uncertainty metrics obtained by a BNN over five of the most used hyperspectral images datasets. In addition, we will illustrate how these metrics can be used for several practical applications such as identifying predictions that do not reach the required level of accuracy, detecting mislabeling in the dataset, or identifying when the predictions are affected by the increase of the level of noise in the input data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021
Journal Articles
Alcolea, Adrián; Resano, Javier
FPGA Accelerator for Gradient Boosting Decision Trees Journal Article
In: Electronics, vol. 10, no. 3, pp. 314, 2021.
@article{alcolea2021fpga,
title = {FPGA Accelerator for Gradient Boosting Decision Trees},
author = {Adrián Alcolea and Javier Resano},
year = {2021},
date = {2021-01-01},
journal = {Electronics},
volume = {10},
number = {3},
pages = {314},
publisher = {Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
Journal Articles
Alcolea, Adrián; Paoletti, Mercedes E; Haut, Juan M; Resano, Javier; Plaza, Antonio
Inference in supervised spectral classifiers for on-board hyperspectral imaging: An overview Journal Article
In: Remote Sensing, vol. 12, no. 3, pp. 534, 2020.
@article{alcolea2020inference,
title = {Inference in supervised spectral classifiers for on-board hyperspectral imaging: An overview},
author = {Adrián Alcolea and Mercedes E Paoletti and Juan M Haut and Javier Resano and Antonio Plaza},
year = {2020},
date = {2020-01-01},
journal = {Remote Sensing},
volume = {12},
number = {3},
pages = {534},
publisher = {Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Haut, Juan M; Alcolea, Adrian; Paoletti, Mercedes E; Plaza, Javier; Resano, Javier; Plaza, Antonio
GPU-Friendly Neural Networks for Remote Sensing Scene Classification Journal Article
In: IEEE Geoscience and Remote Sensing Letters, 2020.
@article{haut2020gpu,
title = {GPU-Friendly Neural Networks for Remote Sensing Scene Classification},
author = {Juan M Haut and Adrian Alcolea and Mercedes E Paoletti and Javier Plaza and Javier Resano and Antonio Plaza},
year = {2020},
date = {2020-01-01},
journal = {IEEE Geoscience and Remote Sensing Letters},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Moreno, Adrián Alcolea; Olivito, Javier; Resano, Javier; Mecha, Hortensia
Analysis of a Pipelined Architecture for Sparse DNNs on Embedded Systems Journal Article
In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 9, pp. 1993–2003, 2020.
@article{moreno2020analysis,
title = {Analysis of a Pipelined Architecture for Sparse DNNs on Embedded Systems},
author = {Adrián Alcolea Moreno and Javier Olivito and Javier Resano and Hortensia Mecha},
year = {2020},
date = {2020-01-01},
journal = {IEEE Transactions on Very Large Scale Integration (VLSI) Systems},
volume = {28},
number = {9},
pages = {1993--2003},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}