39 citations · 63 across the 8 of their papers we have counts for
9 papers
ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving
Salman Khan, Izzeddin Teeti, Reza Javanmard Alitappeh +5
Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happenin…
Deep generative models as an adversarial attack strategy for tabular machine learning
Salijona Dyrmishi, Mihaela Cătălina Stoian, Eleonora Giunchiglia +1
Deep Generative Models (DGMs) have found application in computer vision for generating adversarial examples to test the robustness of machine learning (ML) systems. Extending these…
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…
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…
PiShield: A PyTorch Package for Learning with Requirements
Mihaela Cătălina Stoian, Alex Tatomir, Thomas Lukasiewicz +1
Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introd…
ROAD-R: The Autonomous Driving Dataset with Logical Requirements
Eleonora Giunchiglia, Mihaela Cătălina Stoian, Salman Khan +2
Neural networks have proven to be very powerful at computer vision tasks. However, they often exhibit unexpected behaviours, violating known requirements expressing background know…
Deep Learning with Logical Constraints
Eleonora Giunchiglia, Mihaela Catalina Stoian, Thomas Lukasiewicz
In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) a…
Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds
Mihaela Cătălina Stoian, Tommaso Cavallari
Many man-made objects are characterised by a shape that is symmetric along one or more planar directions. Estimating the location and orientation of such symmetry planes can aid ma…
Analyzing ASR pretraining for low-resource speech-to-text translation
Mihaela C. Stoian, Sameer Bansal, Sharon Goldwater
Previous work has shown that for low-resource source languages, automatic speech-to-text translation (AST) can be improved by pretraining an end-to-end model on automatic speech re…