25 citations · 92 across the 14 of their papers we have counts for
14 papers
Plainbook: Data Science, in Plain Language
Luca de Alfaro, Mathis Aubert, Ranjit Jhala +2
Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis. They are, however, accessible only to people who und…
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 hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding
Alkis Koudounas, Moreno La Quatra, Eliana Pastor +2
Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional neural architectures, yet their application to speech processing remains under exp…
A Synthetic Benchmark to Explore Limitations of Localized Drift Detections
Flavio Giobergia, Eliana Pastor, Luca de Alfaro +1
Concept drift is a common phenomenon in data streams where the statistical properties of the target variable change over time. Traditionally, drift is assumed to occur globally, af…
Detecting Interpretable Subgroup Drifts
Flavio Giobergia, Eliana Pastor, Luca de Alfaro +1
The ability to detect and adapt to changes in data distributions is crucial to maintain the accuracy and reliability of machine learning models. Detection is generally approached b…
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…
A Contrastive Learning Approach to Mitigate Bias in Speech Models
Alkis Koudounas, Flavio Giobergia, Eliana Pastor +1
Speech models may be affected by performance imbalance in different population subgroups, raising concerns about fair treatment across these groups. Prior attempts to mitigate unfa…
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…
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 Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features
Eliana Pastor, Alkis Koudounas, Giuseppe Attanasio +2
Recent advances in eXplainable AI (XAI) have provided new insights into how models for vision, language, and tabular data operate. However, few approaches exist for understanding s…
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
Alan Perotti, Simone Bertolotto, Eliana Pastor +1
Images are loaded with semantic information that pertains to real-world ontologies: dog breeds share mammalian similarities, food pictures are often depicted in domestic environmen…
ITALIC: An Italian Intent Classification Dataset
Alkis Koudounas, Moreno La Quatra, Lorenzo Vaiani +5
Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in…
ferret: a Framework for Benchmarking Explainers on Transformers
Giuseppe Attanasio, Eliana Pastor, Chiara Di Bonaventura +1
As Transformers are increasingly relied upon to solve complex NLP problems, there is an increased need for their decisions to be humanly interpretable. While several explainable AI…
Identifying Biased Subgroups in Ranking and Classification
Eliana Pastor, Luca de Alfaro, Elena Baralis
When analyzing the behavior of machine learning algorithms, it is important to identify specific data subgroups for which the considered algorithm shows different performance with…