Search Results for author: Dario Garcia-Gasulla

Found 19 papers, 6 papers with code

Healthy Twitter discussions? Time will tell

no code implementations21 Mar 2022 Dmitry Gnatyshak, Dario Garcia-Gasulla, Sergio Alvarez-Napagao, Jamie Arjona, Tommaso Venturini

Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies.

Misinformation

Focus! Rating XAI Methods and Finding Biases

1 code implementation28 Sep 2021 Anna Arias-Duart, Ferran Parés, Dario Garcia-Gasulla, Victor Gimenez-Abalos

In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model's behavior using visual cues.

Explainable artificial intelligence Management

The Impact of COVID-19 on Flight Networks

no code implementations4 Jun 2020 Toyotaro Suzumura, Hiroki Kanezashi, Mishal Dholakia, Euma Ishii, Sergio Alvarez Napagao, Raquel Pérez-Arnal, Dario Garcia-Gasulla, Toshiaki Murofushi

As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections.

Obstruction level detection of sewer videos using convolutional neural networks

no code implementations4 Feb 2020 Mario A. Gutierrez-Mondragon, Dario Garcia-Gasulla, Sergio Alvarez-Napagao, Jaume Brossa-Ordoñez, Rafael Gimenez-Esteban

In this work, we design a methodology to train a Convolutional Neural Network for identifying the level of obstruction in pipes, thus reducing the human effort required on such a frequent and repetitive task.

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

Feature discriminativity estimation in CNNs for transfer learning

no code implementations8 Nov 2019 Victor Gimenez-Abalos, Armand Vilalta, Dario Garcia-Gasulla, Jesus Labarta, Eduard Ayguadé

The purpose of feature extraction on convolutional neural networks is to reuse deep representations learnt for a pre-trained model to solve a new, potentially unrelated problem.

Transfer Learning

Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs

1 code implementation21 Dec 2018 Hiroki Kanezashi, Toyotaro Suzumura, Dario Garcia-Gasulla, Min-hwan Oh, Satoshi Matsuoka

We propose an incremental graph pattern matching algorithm to deal with time-evolving graph data and also propose an adaptive optimization system based on reinforcement learning to recompute vertices in the incremental process more efficiently.

Databases

A Visual Distance for WordNet

no code implementations24 Apr 2018 Raquel Pérez-Arnal, Armand Vilalta, Dario Garcia-Gasulla, Ulises Cortés, Eduard Ayguadé, Jesus Labarta

WordNet, which includes a wide variety of concepts associated with words (i. e., synsets), is often used as a source for computing those distances.

Full-Network Embedding in a Multimodal Embedding Pipeline

no code implementations WS 2017 Armand Vilalta, Dario Garcia-Gasulla, Ferran Parés, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura

In this paper we evaluate the impact of using the Full-Network embedding in this setting, replacing the original image representation in a competitive multimodal embedding generation scheme.

Image Retrieval Network Embedding

Building Graph Representations of Deep Vector Embeddings

no code implementations WS 2017 Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés, Jonatan Moreno, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura

Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes.

Graph Embedding

An Out-of-the-box Full-network Embedding for Convolutional Neural Networks

no code implementations ICLR 2018 Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés, Jonatan Moreno, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura

Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option.

General Classification Image Classification +2

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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