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Found 6 papers, 0 papers with code
Estimating the Adversarial Robustness of Attributions in Text with Transformers
Finally, with experiments on several text classification architectures, we show that TEA consistently outperforms current state-of-the-art AR estimators, yielding perturbations that alter explanations to a greater extent while being more fluent and less perceptible.
Fooling Explanations in Text Classifiers
TEF can significantly decrease the correlation between unchanged and perturbed input attributions, which shows that all models and explanation methods are susceptible to TEF perturbations.
On the explainability of hospitalization prediction on a large COVID-19 patient dataset
We develop various AI models to predict hospitalization on a large (over 110$k$) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021.
Artificial Intelligence Decision Support for Medical Triage
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 in use at the largest European telemedicine provider.
FAR: A General Framework for Attributional Robustness
Therefore, we define a novel generic framework for attributional robustness (FAR) as general problem formulation for training models with robust attributions.
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective.
