7 citations · 10 across the 5 of their papers we have counts for
6 papers
Generating Efficient DNN-Ensembles with Evolutionary Computation
Marc Ortiz, Florian Scheidegger, Marc Casas +2
In this work, we leverage ensemble learning as a tool for the creation of faster, smaller, and more accurate deep learning models. We demonstrate that we can jointly optimize for a…
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties
Ferran Parés, Anna Arias-Duart, Dario Garcia-Gasulla +4
In the image classification task, the most common approach is to resize all images in a dataset to a unique shape, while reducing their precision to a size which facilitates experi…
Extending the OpenCHK Model with Advanced Checkpoint Features
Marcos Maroñas, Sergi Mateo, Kai Keller +3
One of the major challenges in using extreme scale systems efficiently is to mitigate the impact of faults. Application-level checkpoint/restart (CR) methods provide the best trade…
Feature discriminativity estimation in CNNs for transfer learning
Victor Gimenez-Abalos, Armand Vilalta, Dario Garcia-Gasulla +2
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. Ho…
MetH: A family of high-resolution and variable-shape image challenges
Ferran Parés, Dario Garcia-Gasulla, Harald Servat +2
High-resolution and variable-shape images have not yet been properly addressed by the AI community. The approach of down-sampling data often used with convolutional neural networks…
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks
Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés +5
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 resour…