3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Exploiting T-norms for Deep Learning in Autonomous Driving
Mihaela Cătălina Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz
Deep learning has been at the core of the autonomous driving field development, due to the neural networks' success in finding patterns in raw data and turning them into accurate p…
cs.LG2024★ 3 cited
How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data
Mihaela Cătălina Stoian, Salijona Dyrmishi, Maxime Cordy +2
Deep Generative Models (DGMs) have been shown to be powerful tools for generating tabular data, as they have been increasingly able to capture the complex distributions that charac…