1 code implementation • 27 Jul 2020 • Ferran Parés, Anna Arias-Duart, Dario Garcia-Gasulla, Gema Campo-Francés, Nina Viladrich, Eduard Ayguadé, Jesús Labarta
In this work we introduce the MAMe dataset, an image classification dataset with remarkable high resolution and variable shape properties.
Ranked #1 on Image Classification on MAMe
no code implementations • 26 Nov 2019 • Raquel Pérez-Arnal, Dario Garcia-Gasulla, David Torrents, Ferran Parés, Ulises Cortés, Jesús Labarta, Eduard Ayguadé
Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development.
1 code implementation • 20 Nov 2019 • Ferran Parés, Dario Garcia-Gasulla, Harald Servat, Jesús Labarta, Eduard Ayguadé
In sight of the increasing importance of problems that can benefit from exploiting high-resolution (HR) and variable-shape, and with the goal of promoting research in that direction, we introduce a new family of datasets (MetH).
2 code implementations • 27 Mar 2017 • Ferran Parés, Dario Garcia-Gasulla, Armand Vilalta, Jonatan Moreno, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, Toyotaro Suzumura
We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction.
Data Structures and Algorithms Social and Information Networks Physics and Society
no code implementations • 3 Mar 2017 • Dario Garcia-Gasulla, Ferran Parés, Armand Vilalta, Jonatan Moreno, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, Toyotaro Suzumura
We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning.
no code implementations • 2 Nov 2016 • Dario Garcia-Gasulla, Eduard Ayguadé, Jesús Labarta, Ulises Cortés
Link prediction, the problem of identifying missing links among a set of inter-related data entities, is a popular field of research due to its application to graph-like domains.