7 citations · 20 across the 6 of their papers we have counts for
6 papers
Estimating the Adversarial Robustness of Attributions in Text with Transformers
Adam Ivankay, Mattia Rigotti, Ivan Girardi +2
Explanations are crucial parts of deep neural network (DNN) classifiers. In high stakes applications, faithful and robust explanations are important to understand and gain trust in…
Fooling Explanations in Text Classifiers
Adam Ivankay, Ivan Girardi, Chiara Marchiori +1
State-of-the-art text classification models are becoming increasingly reliant on deep neural networks (DNNs). Due to their black-box nature, faithful and robust explanation methods…
On the explainability of hospitalization prediction on a large COVID-19 patient dataset
Ivan Girardi, Panagiotis Vagenas, Dario Arcos-Díaz +8
We develop various AI models to predict hospitalization on a large (over 110) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021. Models ra…
Artificial Intelligence Decision Support for Medical Triage
Chiara Marchiori, Douglas Dykeman, Ivan Girardi +6
Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and…
FAR: A General Framework for Attributional Robustness
Adam Ivankay, Ivan Girardi, Chiara Marchiori +1
Attribution maps are popular tools for explaining neural networks predictions. By assigning an importance value to each input dimension that represents its impact towards the outco…
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
Ivan Girardi, Pengfei Ji, An-phi Nguyen +5
We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issu…