Lauriola Ivano

Computer science for societal challenges and innovation, XXXIII series
Grant sponsor

Fabio Aiolli
Alberto Lavelli, Giuseppe Sartori


Project description
Recently, the literature shows that the representation of the data plays a crucial role in machine learning applications. Hence, several methods were born to learn the best representation for a given problem, as is the case of Deep Neural Networks and Multiple Kernel Learning. These methods reduce the human effort in designing good representations while increasing the expressiveness of the learning algorithms. In this project, the representation learning is analyzed from two different viewpoints. The former aims to develop novel technologies and models to learn the representation, mainly focusing on Embeddings, Multiple Kernel Learning, Deep Neural Networks, and their combination. The latter aims to provide a proof-of-concept of these methods on real-world Natural Language Processing tasks, such as the Named Entity Recognition and large-scale document classification in the biomedical domain.