Search Results for author: Jonatan Moreno

Found 4 papers, 1 papers with code

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

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