activity
20162023
most citedExplaining the Explainers in Graph Neural Networks: a Comparative Study

49 citations · 139 across the 23 of their papers we have counts for

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Showing 2023Show all

10 papers · 1 filter

cs.LG2023

Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning

Emanuele Marconato, Andrea Passerini, Stefano Teso

Focus in Explainable AI is shifting from explanations defined in terms of low-level elements, such as input features, to explanations encoded in terms of interpretable concepts lea…

cs.AI2023★ 1 cited

Learning to Guide Human Experts via Personalized Large Language Models

Debodeep Banerjee, Stefano Teso, Andrea Passerini

In learning to defer, a predictor identifies risky decisions and defers them to a human expert. One key issue with this setup is that the expert may end up over-relying on the mach…

physics.soc-ph2023

Adaptation of Student Behavioural Routines during COVID-19: A Multimodal Approach

Nicolò A. Girardini, Simone Centellegher, Andrea Passerini +3

One population group that had to significantly adapt and change their behaviour during the COVID-19 pandemic is students. While previous studies have extensively investigated the i…

cs.AI2023

Egocentric Hierarchical Visual Semantics

Luca Erculiani, Andrea Bontempelli, Andrea Passerini +1

We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow…

cs.LG2023★ 4 cited

Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts

Emanuele Marconato, Stefano Teso, Antonio Vergari +1

Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labe…

cs.AI2023★ 1 cited

Interval Logic Tensor Networks

Samy Badreddine, Gianluca Apriceno, Andrea Passerini +1

In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of r…