Jesús Labarta

Ferran Parés, Anna Arias-Duart, Dario Garcia-Gasulla et al.

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 experimentation at scale. This practice has benefits from a computational perspective, but it entails negative side-effects on performance due to loss of information and image deformation. In this work we introduce the MAMe dataset, an image classification dataset with remarkable high resolution and variable shape properties. The goal of MAMe is to provide a tool for studying the impact of such properties in image classification, while motivating research in the field. The MAMe dataset contains thousands of artworks from three different museums, and proposes a classification task consisting on differentiating between 29 mediums (i.e. materials and techniques) supervised by art experts. After reviewing the singularity of MAMe in the context of current image classification tasks, a thorough description of the task is provided, together with dataset statistics. Experiments are conducted to evaluate the impact of using high resolution images, variable shape inputs and both properties at the same time. Results illustrate the positive impact in performance when using high resolution images, while highlighting the lack of solutions to exploit variable shapes. An additional experiment exposes the distinctiveness between the MAMe dataset and the prototypical ImageNet dataset. Finally, the baselines are inspected using explainability methods and expert knowledge, to gain insights on the challenges that remain ahead.

Raquel Pérez-Arnal, Dario Garcia-Gasulla, David Torrents et al.

Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development. In this paper, we explore a recently released dataset of chromosome rearrangements in 2,586 cancer patients, where different sorts of alterations have been detected. Using a Random Forest classifier, we evaluate the relevance of several features (some directly available in the original data, some engineered by us) related to chromosome rearrangements. This evaluation results in a set of potential tumour genetic markers, some of which are validated in the bibliography, while others are potentially novel.

Ferran Parés, Dario Garcia-Gasulla, Harald Servat et al.

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 is sub-optimal for many tasks, and has too many drawbacks to be considered a sustainable alternative. 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). The four proposed problems include two image classification, one image regression and one super resolution task. Each of these datasets contains thousands of art pieces captured by HR and variable-shape images, labeled by experts at the Metropolitan Museum of Art. We perform an analysis, which shows how the proposed tasks go well beyond current public alternatives in both pixel size and aspect ratio variance. At the same time, the performance obtained by popular architectures on these tasks shows that there is ample room for improvement. To wrap up the relevance of the contribution we review the fields, both in AI and high-performance computing, that could benefit from the proposed challenges.

Dario Garcia-Gasulla, Ferran Parés, Armand Vilalta et al.

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 CNN model (in the case of image data), and reusing it for other purposes is a field of interest, as it provides access to the visual descriptors previously learnt by the CNN after processing millions of images, without requiring an expensive training phase. Contributions to this field (commonly known as feature representation transfer or transfer learning) have been purely empirical so far, extracting all CNN features from a single layer close to the output and testing their performance by feeding them to a classifier. This approach has provided consistent results, although its relevance is limited to classification tasks. In a completely different approach, in this paper we statistically measure the discriminative power of every single feature found within a deep CNN, when used for characterizing every class of 11 datasets. 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. Our results confirm that low and middle level features may behave differently to high level features, but only under certain conditions. We find that all CNN features can be used for knowledge representation purposes both by their presence or by their absence, doubling the information a single CNN feature may provide. We also study how much noise these features may include, and propose a thresholding approach to discard most of it. All these insights have a direct application to the generation of CNN embedding spaces.

Dario Garcia-Gasulla, Eduard Ayguadé, Jesús Labarta et al.

The hyperlink prediction task, that of proposing new links between webpages, can be used to improve search engines, expand the visibility of web pages, and increase the connectivity and navigability of the web. Hyperlink prediction is typically performed on webgraphs composed by thousands or millions of vertices, where on average each webpage contains less than fifty links. Algorithms processing graphs so large and sparse require to be both scalable and precise, a challenging combination. Similarity-based algorithms are among the most scalable solutions within the link prediction field, due to their parallel nature and computational simplicity. These algorithms independently explore the nearby topological features of every missing link from the graph in order to determine its likelihood. Unfortunately, the precision of similarity-based algorithms is limited, which has prevented their broad application so far. In this work we explore the performance of similarity-based algorithms for the particular problem of hyperlink prediction on large webgraphs, and propose a novel method which assumes the existence of hierarchical properties. We evaluate this new approach on several webgraphs and compare its performance with that of the current best similarity-based algorithms. Its remarkable performance leads us to argue on the applicability of the proposal, identifying several use cases of hyperlink prediction. We also describes the approach we took for the computation of large-scale graphs from the perspective of high-performance computing, providing details on the implementation and parallelization of code.

Dario Garcia-Gasulla, Eduard Ayguadé, Jesús Labarta et al.

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. Producing consistent evaluations of the performance of the many link prediction algorithms being proposed can be challenging due to variable graph properties, such as size and density. In this paper we first discuss traditional data mining solutions which are applicable to link prediction evaluation, arguing about their capacity for producing faithful and useful evaluations. We also introduce an innovative modification to a traditional evaluation methodology with the goal of adapting it to the problem of evaluating link prediction algorithms when applied to large graphs, by tackling the problem of class imbalance. We empirically evaluate the proposed methodology and, building on these findings, make a case for its importance on the evaluation of large-scale graph processing.