Jorge Calvo-Zaragoza has been awarded with the prize to the best novel researcher of the year in Computer Science in Spain: https://www.fbbva.es/noticias/coches-sin-conductor-realidad-aumentada-ordenadores-leen-emociones-los-premios-informatica-abordan-lo
New paper accepted: Antonio-Javier Gallego, Antonio Pertusa, Jorge Calvo-Zaragoza. “Improving Convolutional Neural Networks accuracy in noisy environments using k-Nearest Neighbors”. In: Applied Sciences.
New paper accepted: Unai Garay-Maestre, Antonio-Javier Gallego and Jorge Calvo-Zaragoza. “Data Augmentation via Variational Auto-Encoders”. In: 23rd Iberoamerican Congress on Pattern Recognition (CIARP 2018)
New paper accepted: Javier Ortega-Bastida, Antonio-Javier Gallego and Antonio Pertusa. “Multimodal Object Recognition using Deep Learning Representations extracted from Images and Smartphone Sensors”. In: 23rd Iberoamerican Congress on Pattern Recognition
New communication presented: José M. Iñesta presented “HISPAMUS: Handwritten Spanish Music Heritage Preservation by Automatic Transcription” at WoRMS 2018 workshop.
New paper accepted: Jorge Calvo-Zaragoza and Antonio-Javier Gallego. “A selectional auto-encoder approach for document image binarization”. In: Pattern Recognition.
New paper accepted: J.M. Iñesta, P.J. Ponce de León, D. Rizo, J. Oncina, L. Micó, J.R. Rico-Juan, C. Pérez-Sancho, A. Pertusa. “HISPAMUS: Handwritten Spanish Music Heritage Preservation by Automatic Transcription”. In 1st Workshop on Reading Music Systems
New paper accepted: Aurelia Bustos, Antonio Pertusa. “Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks”. In: Applied Sciences.
New paper accepted: Juan Ramón Rico-Juan, Antonio-Javier Gallego, Jose J. Valero-Mas, and Jorge Calvo-Zaragoza. “Statistical semi-supervised system for grading multiple peer-reviewed open-ended works”. In Journal of Computers & Education.
New communication presented: José M. Iñesta, Luisa Micó, David Rizo. “Incremental learning for recognition of handwritten mensural notation”. In ICML joint workshop on Machine Learning for Music.