Publications

• Journal

  • Laparra, V., Pérez-Suay, A., Piles, M., Muñoz-Marí, J., Amorós, J., Fernandez-Moran, R., Fernández-Torres, M. Á., & Adsuara, J. E. (2023). Assessing the Impact of Using Short Videos for Teaching at Higher Education: Empirical evidence from log-files in a Learning Management System. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje. DOI: 10.1109/RITA.2023.3301411

  • Kondylatos, S., Prapas, I., Ronco, M., Papoutsis, I., Camps‐Valls, G., Piles, M., Fernández-Torres, M. Á., & Carvalhais, N. (2022). Wildfire danger prediction and understanding with Deep Learning. Geophysical Research Letters, e2022GL099368. DOI: 10.1029/2022GL09938

  • Martínez-Cebrián, J., Fernández-Torres, M. Á., & Díaz-de-María, F. Interpretable Global-Local Dynamics for the Prediction of Eye Fixations in Autonomous Driving Scenarios. IEEE Access, 8, 217068-217085. DOI: 10.1109/ACCESS.2020.3041606

  • Fernández-Torres, M. Á., González-Díaz, I., & Díaz-de-María, F. (2019). Probabilistic Topic Model for Context-Driven Visual Attention Understanding. IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 6, pp. 1653-1667, June 2020. DOI: 10.1109/TCSVT.2019.2909427

  • Fernández-Martínez, F., Hernández-García, A., Fernández-Torres, M. A., González-Díaz, I., García-Faura, Á., & de María, F. D. (2018). Exploiting visual saliency for assessing the impact of car commercials upon viewers. Multimedia Tools and Applications, 77(15), 18903-18933. DOI:10.1007/s11042-017-5339-9

  • López-Labraca, J., Fernández-Torres, M. Á., González-Díaz, I., Díaz-de-María, F., & Pizarro, Á. (2018). Enriched dermoscopic-structure-based cad system for melanoma diagnosis. Multimedia Tools and Applications, 77(10), 12171-12202. DOI: 10.1007/s11042-017-4879-3

• International Conferences

  • Pyrina, M., Wu, Z., Bommer, P. L., Fernández-Torres, M. Á., & Domeisen, D. (2023). Sub-seasonal prediction of drought over the Horn of Africa with Neural Networks. In XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG). GFZ German Research Centre for Geosciences. DOI: 10.57757/IUGG23-1811

  • Höhl, A., Ofori-Ampofo, S., Obadic, I., Fernández-Torres, M. Á., Kuzu, R. S., & Zhu, X. (2023). USCC: A Benchmark Dataset for Crop Yield Prediction under Climate Extremes (No. EGU23-15540). Copernicus Meetings.

  • Mora, K., Brandt, G., Benson, V., Brockmann, C., Camps-Valls, G., Fernández-Torres, M. Á., … & Mahecha, M. D. (2023). DeepExtremes: Explainable Earth Surface Forecasting Under Extreme Climate Conditions (No. EGU23-12657). Copernicus Meetings.

  • Cortés-Andrés, J., Gonzalez-Calabuig, M., Zhang, M., Williams, T., Fernández-Torres, M. Á., Pellicer-Valero, O. J., & Camps-Valls, G. (2023). XAIDA4Detection: A Toolbox for the Detection and Characterization of Spatio-Temporal Extreme Events (No. EGU23-4816). Copernicus Meetings.

  • Zhang, M., Fernández-Torres, M. Á., and Camps-Valls, G. Hybrid Recurrent Neural Network for Drought Monitoring. In Workshop Tackling Climate Change with Machine Learning at the 2022 36th Conference on Neural Information Processing Systems (NeurIPS). https://www.climatechange.ai/papers/neurips2022/51

  • Varando, G., Fernández-Torres, M. Á., Muñoz-Marí, J., and Camps-Valls, G. Learning Causal Representations with Granger PCA. In UAI 2022 Workshop on Causal Representation Learning. https://openreview.net/pdf?id=XsTEnaD_Lel

  • Fernández-Torres, M. Á., Ronco, M., Benson, V., Requena-Mesa, C., Mahecha, M. and Camps-Valls, G. Explaining Deep Learning Models for Earth Surface Forecasting. In Living Planet Symposium 2022 (Poster), 23–27 May 2022.

  • González-Calabuig, M., Fernández-Torres, M. Á., Moreno-Martínez, Á. and Camps-Valls, G. Unsupervised Deep Learning for Spatio-Temporal Earth Data Interpolation and Gap Filling. In Living Planet Symposium 2022 (Poster), 23–27 May 2022.

  • Mahecha, M., Gans, F., Camps-Valls, G., Brandt, G., Kraemer, G., Mora, K., Fernández-Torres, M. Á., Requena-Mesa, C., Benson, V., Reichstein, M., Brockmann, C. and Ronco, M., DeepExtremes - Deploying Artificial Experiments on High-Resolution Data Cubes for Explaining Extreme Event Impacts. In Living Planet Symposium 2022 (Poster), 23–27 May 2022.

  • Kondylatos, S., Prapas, I., Papoutsis, I., Camps-Valls, G. and Ronco, M., Fernández-Torres, M. Á., Guillem, M.P. and Carvalhais, N., Deep Learning Methods for Daily Wildfire Danger Forecasting. In Living Planet Symposium 2022 (Poster), 23–27 May 2022.

  • Ronco, M., Prapas, I., Kondylatos, S., Papoutsis, I., Camps-Valls, G., Fernández-Torres, M. Á., Piles, María and Carvalhais, N. (2022). Explainable deep learning for wildfire danger estimation (No. EGU22-11787). Copernicus Meetings.

  • González-Calabuig, M., Cort'es-Andrés, J., Fernández-Torres, M. Á., and Camps-Valls, G. (2022). Recent Advances in Deep Learning for Spatio-Temporal Drought Monitoring, Forecasting and Model Understanding. EGU22, (EGU22-11872).

  • Varando, G., Fernández-Torres, M. Á., and Camps-Valls, G. (2022). Learning ENSO-related Principal Modes of Vegetation via a Granger-Causal Variational Autoencoder (No. EGU22-11388). Copernicus Meetings.

  • Cortés-Andrés, J., Fernández-Torres, M. Á., and Camps-Valls, G. (2021, December). Location-Aware Convolutional Encoder-Decoder for Drought Detection in Europe. In AGU Fall Meeting 2021. AGU. https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/836661

  • Varando, G., Fernández-Torres, M. Á., and Camps-Valls, G. (2021, December). Learning Granger Causal Feature Representations. In AGU Fall Meeting 2021. AGU. https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/857866

  • Prapas, I., Kondylatos, S., Papoutsis, I., Camps-Valls, G., Ronco, M., Fernández-Torres, M. Á., Guillem, M.P. and Carvalhais, N., 2021. Deep Learning Methods for Daily Wildfire Danger Forecasting. In Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response at the 2021 35th Conference on Neural Information Processing Systems (NeurIPS) https://doi.org/10.48550/arXiv.2111.02736

  • Varando, G., Fernández-Torres, M. Á., and Camps-Valls, G. Learning Granger Causal Feature Representations. In Workshop Tackling Climate Change with Machine Learning at the 2021 38th International Conference on Machine Learning (ICML). https://www.climatechange.ai/papers/icml2021/34

  • Fernández-Torres, M. Á. (2021, June). Deep Learning and Explainable AI for Spatio-Temporal Drought Monitoring. In Second ITU/WMO/UNEP Workshop on AI for Natural Disaster Management (Oral presentation). https://www.itu.int/en/ITU-T/Workshops-and-Seminars/2021/0623/Pages/default.aspx

  • Fernández-Torres, M. Á., Johnson, J. E., Piles, M., & Camps-Valls, G. (2021). Spatio-Temporal Gaussianization Flows for Extreme Event Detection (No. EGU21-15729). Copernicus Meetings. DOI: 10.5194/egusphere-egu21-15729

  • Fernández-Torres, M. Á., González-Díaz, I., & Díaz-de-María, F. (2016, June). A probabilistic topic approach for context-aware visual attention modeling. In 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) (pp. 1-6). IEEE. DOI: 10.1109/CBMI.2016.7500272

  • Martínez-Cortés, T., Fernández-Torres, M. Á., Jiménez-Moreno, A., González-Díaz, I., Díaz-de-María, F., Guzmán-De-Villoria, J. A., & Fernández, P. (2014, October). A Bayesian model for brain tumor classification using clinical-based features. In 2014 IEEE International Conference on Image Processing (ICIP) (pp. 2779-2783). IEEE. DOI: 10.1109/ICIP.2014.7025562