{"id":3819,"date":"2023-01-24T10:50:03","date_gmt":"2023-01-24T08:50:03","guid":{"rendered":"https:\/\/gaz-temporal.i3a.es\/?p=3819"},"modified":"2025-05-05T14:47:56","modified_gmt":"2025-05-05T12:47:56","slug":"adrian-alcolea-moreno","status":"publish","type":"post","link":"https:\/\/gaz.i3a.es\/es\/adrian-alcolea-moreno\/","title":{"rendered":"Adri\u00e1n Alcolea Moreno"},"content":{"rendered":"
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    Investigadores<\/a><\/h3>\n

    Adri\u00e1n Alcolea Moreno<\/strong><\/h1>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/li>\n\t\t<\/ul>\t\t\t\t
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      Assistant Professor<\/strong><\/p>\n

      Email:<\/strong> alcolea@unizar.es<\/a><\/p>\n

      Address:<\/strong> Campus R\u00edo Ebro, University of Zaragoza
      \nC\/Mar\u00eda de Luna 1, Ada Byron Building,
      \n50018, Zaragoza, Spain<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n

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      \n\t\t\t\t\t\tABOUT ME\t\t\t\t\t\t<\/h5>\n\t\t\t\t\t\t\t\t\t\t\t
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      I am an assistant professor in the Computer Architecture Group (gaZ) at the University of Zaragoza.<\/p>\n

      Before that, I graduated in\u00a0Social Work<\/a>\u00a0from the University of Zaragoza, Spain (2010), I obtained my master degree in\u00a0International Peace, Conflict and Development Studies<\/a>\u00a0from the\u00a0University Jaume I of Castell\u00f3n<\/a>, Spain (2012), I graduated in Informatics Engineering<\/a> from the University of Zaragoza, Spain (2017), and then I received my PhD in System Engineering and Computer Science<\/a> at the University of Zaragoza, Spain ().<\/p>\n

      Current research<\/strong><\/p>\n

      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.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>

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      7 registros<\/span> «<\/a> ‹<\/a> 1 de 2 ›<\/a> »<\/a> <\/div><\/div>

      2022<\/h3>

      Art\u00edculos de revista<\/h3>

      Alcolea, Adri\u00e1n; Resano, Javier<\/p>

      Bayesian Neural Networks to Analyze Hyperspectral Datasets Using Uncertainty Metrics<\/a> Art\u00edculo de revista<\/span> <\/p>

      En: <\/span>IEEE Transactions on Geoscience and Remote Sensing, <\/span>vol. 60, <\/span>pp. 1-10, <\/span>2022<\/span>, ISSN: 1558-0644<\/span>.<\/p>

      Resumen<\/a><\/span> | Enlaces<\/a><\/span> | BibTeX<\/a><\/span><\/p>

      @article{9881579,
      \r\ntitle = {Bayesian Neural Networks to Analyze Hyperspectral Datasets Using Uncertainty Metrics},
      \r\nauthor = {Adri\u00e1n Alcolea and Javier Resano},
      \r\ndoi = {10.1109\/TGRS.2022.3205119},
      \r\nissn = {1558-0644},
      \r\nyear  = {2022},
      \r\ndate = {2022-01-01},
      \r\njournal = {IEEE Transactions on Geoscience and Remote Sensing},
      \r\nvolume = {60},
      \r\npages = {1-10},
      \r\nabstract = {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.},
      \r\nkeywords = {},
      \r\npubstate = {published},
      \r\ntppubtype = {article}
      \r\n}
      \r\n<\/pre><\/div>
      Cerrar<\/a><\/p><\/div>
      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.<\/div>
      Cerrar<\/a><\/p><\/div>