Search Results for author: Jesús Labarta

Found 6 papers, 3 papers with code

Random Forest as a Tumour Genetic Marker Extractor

no code implementations26 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.

MetH: A family of high-resolution and variable-shape image challenges

1 code implementation20 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).

Image Classification Super-Resolution

Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm

2 code implementations27 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

On the Behavior of Convolutional Nets for Feature Extraction

no code implementations3 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.

Representation Learning Transfer Learning

Limitations and Alternatives for the Evaluation of Large-scale Link Prediction

no code implementations2 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.

Link Prediction

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