no code implementations • CVPR 2021 • Alejandro Hernandez Ruiz, Armand Vilalta, Francesc Moreno-Noguer
In biological terms, our approach would play the role of the transcription factors, modulating the mapping of genes into specific proteins that drive cellular differentiation, which occurs right before the morphogenesis.
no code implementations • 8 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.
no code implementations • 24 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.
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.
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.
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.
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.