23 citations · 65 across the 9 of their papers we have counts for
9 papers
HydroChronos: Forecasting Decades of Surface Water Change
Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor +4
Forecasting surface water dynamics is crucial for water resource management and climate change adaptation. However, the field lacks comprehensive datasets and standardized benchmar…
KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation
Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor +3
Segmentation of crop fields is essential for enhancing agricultural productivity, monitoring crop health, and promoting sustainable practices. Deep learning models adopted for this…
Voice Disorder Analysis: a Transformer-based Approach
Alkis Koudounas, Gabriele Ciravegna, Marco Fantini +4
Voice disorders are pathologies significantly affecting patient quality of life. However, non-invasive automated diagnosis of these pathologies is still under-explored, due to both…
A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data
Eleonora Poeta, Flavio Giobergia, Eliana Pastor +2
Kolmogorov-Arnold Networks (KANs) have very recently been introduced into the world of machine learning, quickly capturing the attention of the entire community. However, KANs have…
Linearly-Interpretable Concept Embedding Models for Text Analysis
Francesco De Santis, Philippe Bich, Gabriele Ciravegna +3
Despite their success, Large-Language Models (LLMs) still face criticism due to their lack of interpretability. Traditional post-hoc interpretation methods, based on attention and…
Concept-based Explainable Artificial Intelligence: A Survey
Eleonora Poeta, Gabriele Ciravegna, Eliana Pastor +2
The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models. However, using raw features to provide explanatio…
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…