Author Archives: admin

Explainability is a topic of growing importance in NLP. In this work, we provide a unified perspective of explainability as a communication problem between an explainer and a layperson about a classifier’s decision. We use this framework to compare several … Read More

Whereas physical obstacles were mostly associated with signal attenuation in telecommunications, their presence in 5G’s millimeter wave systems adds complex, non-linear phenomena, including reflections and scattering. The result is a multipath propagation environment, shaped by the obstacles encountered during transmission, … Read More

In order to make decisions, for instance when purchasing a product, people rely on rich and accurate descriptions, which entail multi-label retrieval processes. However, multi-label classification is challenged by high dimensional and complex feature spaces and its dependency on large … Read More

Attention mechanisms have become ubiquitous in NLP. Recent architectures, notably the Transformer, learn powerful context-aware word representations through layered, multi-headed attention. The multiple heads learn diverse types of word relationships. However, with standard softmax attention, all attention heads are dense, … Read More

Duplicate detection concerns with identifying pairs of attributes/records that refer to the same real-world object, thus corresponding to a fundamental process when ensuring data quality in databases. Existing methods to detect duplicate attributes can leverage heuristic string similarity measures based … Read More

Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts. However, the most successful learning algorithm for artificial neural networks, backpropagation, is considered biologically implausible. Many believe that the next generation … Read More

In the past few years, deep generative models, such as generative adversarial networks, variational autoencoders, and their variants, have seen wide adoption for the task of modelling complex data distributions. In spite of the outstanding sample quality achieved by those … Read More

Visual attention mechanisms are widely used in multimodal tasks, such as image captioning and visual question answering (VQA), being softmax attention mechanism the standard choice. One drawback of softmax-based attention mechanisms is that they assign probability mass to all image … Read More

We’ll discuss how Priberam is applying machine learning to the problems of Media Monitoring and Technology Watch in the context of the INSIGHT P2020 project. We’ll show a working prototype and describe the developed microservices platform, and how the several … Read More

Nowadays we are collecting high-dimensional and large data streams, where many dimensions can be expressing basically the same information on the underlying process of interest. This redundancy is apparent, for example, if we observe mass media news outputs through time. … Read More