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. Data and methods for a visual understanding of sign languages. . Signal Theory and Communications. 2022.
. Deep Learning Representations for All (a.ka. the AI hype). 2021. 
(10.95 MB)
(10.95 MB). Deep variational autoencoders for population genetics: applications in classification, imputation, dimensionality reduction, and novel lossless data compression. In American Society of Human Genetics (ASHG). Virtual: ASHG; 2021.
. Discovery and Learning of Navigation Goals from Pixels in Minecraft. . 2021. (16.15 MB)
(16.15 MB). Disentangling neural network structure from the weights space. . 2021. (17.1 MB)
(17.1 MB). A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 imagery. Remote Sensing. 2021;13(22):4547.
. Deep Learning that Scales: Leveraging Compute and Data. . Computer Architecture. [Barcelona, Catalonia]: Universitat Politècnica de Catalunya; 2020. (8.55 MB)
(8.55 MB)Deep Learning Representations for All (a.k.a. the AI hype). 2019. (10.95 MB)
(10.95 MB). Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network. Sensors. 2019;19(8). (4.75 MB)
(4.75 MB). Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI. In Alzheimer's Association International Conference. 2019.
. Detection of Amyloid-Positive Cognitively Unimpaired Individuals Using Voxel-Based Machine Learning on Structural Longitudinal Brain MRI. Alzheimer's & Dementia. 2019;15(754).
. Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas. British Journal of Haematology. 2019;184(3):373-383.
. Digitally Stained Confocal Microscopy through Deep Learning. In International Conference on Medical Imaging with Deep Learning (MIDL 2019). London; 2019.
. Demonstration of an Open Source Framework for Qualitative Evaluation of CBIR Systems. In ACM Multimedia. Seoul, South Korea: ACM; 2018. (11.05 MB)
(11.05 MB). Detection-aided liver lesion segmentation using deep learning. In ML4H: Machine Learning for Health Workshop at NIPS 2017. 2017. (1 MB)
(1 MB). Detection-aided medical image segmentation using deep learning. . 2017. (7.07 MB)
(7.07 MB). Disentangling Motion, Foreground and Background Features in Videos. In CVPR 2017 Workshop Brave New Motion Representations. 2017. (370.64 KB)
(370.64 KB). Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster. In International Conference on Computational Science (ICCS). Zurich, Switzerland: Elsevier; 2017. (576.67 KB)
(576.67 KB). Depth map compression via 3D region-based representation. Multimedia Tools and Applications. 2016;. (15.02 MB)
(15.02 MB). Detect Snap Points in Egocentric Images with Physiological Signals. . 2016. (4.63 MB)
(4.63 MB). Dublin City University and Partners' Participation in the INS and VTT Tracks at TRECVid 2016. In TRECVID Workshop 2016. Gaithersburg, MD, USA; 2016.