13 citations · 13 across the 4 of their papers we have counts for
5 papers
Transfer Learning for Evolving Domains
Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira +4
Transfer learning explores how to leverage knowledge from various tasks or domains (sources) to enhance predictive performance in related tasks or domains (targets). Typically, tra…
Evaluating Transfer Learning Methods on Real-World Data Streams: A Case Study in Financial Fraud Detection
Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira +3
When the available data for a target domain is limited, transfer learning (TL) methods can be used to develop models on related data-rich domains, before deploying them on the targ…
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +3
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on f…
The GANfather: Controllable generation of malicious activity to improve defence systems
Ricardo Ribeiro Pereira, Jacopo Bono, João Tiago Ascensão +3
Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. I…
Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +4
Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1.7-4 trillion euros annually) such as drug dealing, human trafficking, or corruptio…