5 citations · 9 across the 6 of their papers we have counts for
6 papers
A General Framework for Property-Driven Machine Learning
Thomas Flinkow, Marco Casadio, Colin Kessler +2
Neural networks have been shown to frequently fail to learn critical safety and correctness properties purely from data, highlighting the need for training methods that directly in…
Neural Network Verification for Gliding Drone Control: A Case Study
Colin Kessler, Ekaterina Komendantskaya, Marco Casadio +5
As machine learning is increasingly deployed in autonomous systems, verification of neural network controllers is becoming an active research domain. Existing tools and annual veri…
NLP Verification: Towards a General Methodology for Certifying Robustness
Marco Casadio, Tanvi Dinkar, Ekaterina Komendantskaya +6
Machine Learning (ML) has exhibited substantial success in the field of Natural Language Processing (NLP). For example large language models have empirically proven to be capable o…
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification
Marco Casadio, Luca Arnaboldi, Matthew L. Daggitt +5
Verification of machine learning models used in Natural Language Processing (NLP) is known to be a hard problem. In particular, many known neural network verification methods that…
Why Robust Natural Language Understanding is a Challenge
Marco Casadio, Ekaterina Komendantskaya, Verena Rieser +4
With the proliferation of Deep Machine Learning into real-life applications, a particular property of this technology has been brought to attention: robustness Neural Networks noto…
Neural Network Robustness as a Verification Property: A Principled Case Study
Marco Casadio, Ekaterina Komendantskaya, Matthew L. Daggitt +4
Neural networks are very successful at detecting patterns in noisy data, and have become the technology of choice in many fields. However, their usefulness is hampered by their sus…