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A subgroup discovery-based method has recently been proposed to understand the behavior of models in the (original) feature space. The subgroups identified represent areas of feature space where the model obtains better or worse predictive performance than on average. For instance, in the marketing domain, the approach extracts subgroups such as: for customers with higher income and who are younger, the random forest achieves higher...

Robotic navigation in environments shared with other robots or humans remains challenging as the intentions of the surrounding agents are not directly observable. Moreover, interaction is crucial to enable safe and efficient navigation in crowded scenarios. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require global guidance and trajectory predictions for the other agents, which is not...

Nowadays, generative question-answering models (e.g., UnifiedQA) achieve state-of-the-art performance in various datasets. Despite their remarkable performance, these models still produce wrong answers with high confidence scores. The responsible use of such systems in high-risk applications, like healthcare, requires some guarantees in terms of the correlation of the model scores and the output’s correctness. One potential approach toward these guarantees is calibration. Despite the vast research...

The use of deep learning algorithms in the clinical context is hindered by their lack of interpretability. One way of increasing the acceptance of such complex algorithms is by providing explanations of the decisions through the presentation of similar examples. Besides helping to understand model behaviour, the presentation of similar disease-related examples, also supports the decision-making process of the radiologist or clinician under challenging diagnosis...

Geometric deep learning is an emerging area of research in machine learning focusing on exploiting symmetries in problems to improve models. Its goal is to understand how transformations to the input should affect the output and design neural networks around the corresponding inductive bias. We present a message passing neural network architecture designed to be equivariant to column and row permutations of a matrix. We...

Geometric deep learning is an emerging area of research in machine learning focusing on exploiting symmetries in problems to improve models. Its goal is to understand how transformations to the input should affect the output and design neural networks around the corresponding inductive bias. We present a message passing neural network architecture designed to be equivariant to column and row permutations of a matrix. We...

The task of organizing and clustering multilingual news articles for media monitoring is essential to follow news stories in real time. Most approaches to this task focus on high-resource languages, with low-resource languages being disregarded. With that in mind, we present a much simpler online system that is able to cluster an incoming stream of documents without depending on language-specific features. We empirically demonstrate that...

The growing importance of the Explainable Artificial Intelligence (XAI) field has resulted in the proposal of several methods for producing visual heatmaps of the classification decisions of deep learning models. However, visual explanations are not enough since different end-users have different backgrounds and preferences. Natural language explanations (NLEs) are inherently understandable by humans and, thus, can complement visual explanations. In the literature, the problem of...