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of 19
pro vyhledávání: '"Vilalta, Armand"'
Very recently, the Neural Cellular Automata (NCA) has been proposed to simulate the morphogenesis process with deep networks. NCA learns to grow an image starting from a fixed single pixel. In this work, we show that the neural network (NN) architect
Externí odkaz:
http://arxiv.org/abs/2006.12155
Autor:
Gimenez-Abalos, Victor, Vilalta, Armand, Garcia-Gasulla, Dario, Labarta, Jesus, Ayguadé, Eduard
Publikováno v:
Volume 319: Artificial Intelligence Research and Development 2019
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. However, raw feature extraction from all layers is unfeasible given the
Externí odkaz:
http://arxiv.org/abs/1911.03332
Autor:
Pérez-Arnal, Raquel, Vilalta, Armand, Garcia-Gasulla, Dario, Cortés, Ulises, Ayguadé, Eduard, Labarta, Jesus
Measuring the distance between concepts is an important field of study of Natural Language Processing, as it can be used to improve tasks related to the interpretation of those same concepts. WordNet, which includes a wide variety of concepts associa
Externí odkaz:
http://arxiv.org/abs/1804.09558
Autor:
Béjar, Javier, Pérez, Raquel, Vilalta, Armand, Álvarez-Napagao, Sergio, Garcia-Gasulla, Dario
Publikováno v:
In Expert Systems With Applications 15 July 2022 198
Autor:
Vilalta, Armand, Garcia-Gasulla, Dario, Parés, Ferran, Ayguadé, Eduard, Labarta, Jesus, Cortés, Ulises, Suzumura, Toyotaro
The current state-of-the-art for image annotation and image retrieval tasks is obtained through deep neural networks, which combine an image representation and a text representation into a shared embedding space. In this paper we evaluate the impact
Externí odkaz:
http://arxiv.org/abs/1707.09872
Autor:
Garcia-Gasulla, Dario, Vilalta, Armand, Parés, Ferran, Moreno, Jonatan, Ayguadé, Eduard, Labarta, Jesus, Cortés, Ulises, Suzumura, Toyotaro
Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes. Typically, deep network representations are implemented within vector embedding spaces, which enabl
Externí odkaz:
http://arxiv.org/abs/1707.07465
Autor:
Garcia-Gasulla, Dario, Vilalta, Armand, Parés, Ferran, Moreno, Jonatan, Ayguadé, Eduard, Labarta, Jesus, Cortés, Ulises, Suzumura, Toyotaro
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
Externí odkaz:
http://arxiv.org/abs/1705.07706
Autor:
Parés, Ferran, Garcia-Gasulla, Dario, Vilalta, Armand, Moreno, Jonatan, Ayguadé, Eduard, Labarta, Jesús, Cortés, Ulises, Suzumura, Toyotaro
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. Fluid Communities is based on the propagation methodology, which re
Externí odkaz:
http://arxiv.org/abs/1703.09307
Autor:
Garcia-Gasulla, Dario, Parés, Ferran, Vilalta, Armand, Moreno, Jonatan, Ayguadé, Eduard, Labarta, Jesús, Cortés, Ulises, Suzumura, Toyotaro
Deep neural networks are representation learning techniques. During training, a deep net is capable of generating a descriptive language of unprecedented size and detail in machine learning. Extracting the descriptive language coded within a trained
Externí odkaz:
http://arxiv.org/abs/1703.01127
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