The main goal of the project is to find correspondences between points in two 3D point clouds using deep learning. A deep learning network is trained to select key points and find correspondences between two point clouds ...

In this work, we explore the evolution of Sequential Neural Models, and their use as a Summarizer System. Transformer is a recently proposed model with a high potential. We experiment and compare their result in abstractive ...

In this project, our work can be divided into two parts: RGB-D based action recognition in trimmed videos and temporal action detection in untrimmed videos.

In this master thesis, two novel families of methods in the field of Case-Base Maintenance have been presented. Both of the proposed methods have successful results and one of them even surpass well-known state-of-the-art ...

Adaptive techniques based on machine learning and data mining are gaining relevance in self-management and self- defense for networks and distributed systems. In this paper, we focus on early detection and stopping of ...

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key ...

Algorithmic composition systems are now well-understood. However, when they are used for specific tasks like creating material for a part of a piece, it is common to prefer, from all of its possible outputs, those exhibiting ...

The aim of this project is the study of reinforcement learning algorithms for micro-robots. It begins with the description of the collective intelligence concept and its main characteristics. Then it presents concepts such ...

This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and ...