Connectivity Fund

Anna Vettoruzzo PhD student at the Halmstad University Continual learning (CL) refers to the ability to continually learn over time by accommodating new knowledge while retaining previously learned experiences. While this concept is inherent in the human learning ability, current machine learning-based methods struggle with this as they are highly prone to forget past experiences […]

Efrén Rama Maneiro PhD student at the University of Santiago de Compostela Predictive monitoring is a subfield of process mining that focuses on predicting how a process will unfold. Deep learning techniques have become popular in this field due to their enhanced performance with respect to classic machine learning models. However, most of these approaches

Prabhant Singh Research Engineer at TU Eindhoven Automated machine learning (AutoML) aims to make easier and more accessible use of machine learning algorithms for researchers with varying levels of expertise. However, AutoML systems, including classical ones such as Auto-Sklearn and Neural Architecture Search (NSGANet, ENAS, DARTS), still face challenges with starting from scratch for their

Paolo Turrini Associate Professor at the Department of Computer Science, University of Warwick This project studies rating systems with multiple objectives, where users are matched to items in order to satisfy several desirable properties. In particular, it looks beyond classical Pareto efficiency, modelling and studying allocations that satisfy fairness, diversity and reliability. This project will

Simone Milanesi, Ambrogio Maria Bernardelli PhD students at the CompOpt Lab (University of Pavia) Binarized Neural Networks (BNNs) are receiving increasing attention due to their lightweight architecture and ability to run on low-power devices.The Mixed-Integer Linear Programming (MILP) approach achieves the state of the art for training classification BNNs when limited data are available.We propose

Meelis Kull Associate Professor at the University of Tartu he field of Machine Learning continues to grow tremendously and has a significant impact on society. As such, it is important to democratize machine learning, i.e. to make sure that software, datasets, models, and analyses are freely available for easy discovery, verifiability, reproducibility, reuse and meta-analysis.

Maria Tsfasman PhD student at TU Delft As AI applications continue to proliferate in our daily lives, the need for social intelligence in these systems becomes increasingly crucial. To enable long-term performance of social intelligence, AI systems must be aware of important moments, or “hotspots” in user conversations. Till now, conversational hotspots have been mainly

Mariangela Graziano PhD student at Università degli Studi della Campania “L. Vanvitelli” Natural Language Processing (NLP) is an area of artificial intelligence (AI) that deals with giving computers the ability to understand text and spoken words in the same way that people do. As textual data is now everywhere: documents on our PCs or in

Luigi Colucci Cante PhD student at Università degli Studi della Campania “Luigi Vanvitelli” For several years, a very ambitious process has been going on to digitalize the entire public administration system relating to the services offered to the public. In an attempt to facilitate the implementation of this transformation, the use of standards for the

Ivica Obadic Chair of Data Science in Earth Observation at the Technical University of Munich The increasingly available high-resolution satellite data has shown to be a valuable resource in tackling pressing issues related to climate change and urbanization such as flood detection. In recent years, deep learning models based on satellite data have shown to be