3 citations · 3 across the 2 of their papers we have counts for
3 papers
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features
Francesco Ventura, Salvatore Greco, Daniele Apiletti +1
Despite the high accuracy offered by state-of-the-art deep natural-language models (e.g. LSTM, BERT), their application in real-life settings is still widely limited, as they behav…
What's in the box? Explaining the black-box model through an evaluation of its interpretable features
Francesco Ventura, Tania Cerquitelli
Algorithms are powerful and necessary tools behind a large part of the information we use every day. However, they may introduce new sources of bias, discrimination and other unfai…
Automating concept-drift detection by self-evaluating predictive model degradation
Tania Cerquitelli, Stefano Proto, Francesco Ventura +2
A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to s…