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Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models
David Debot, Giuseppe Marra
Concept Bottleneck Models (CBNMs) are deep learning models that provide interpretability by enforcing a bottleneck layer where predictions are based exclusively on human-understand…
Foundations of Interpretable Models
Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine +2
We argue that existing definitions of interpretability are not actionable in that they fail to inform users about general, sound, and robust interpretable model design. This makes…
Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning
David Debot, Pietro Barbiero, Gabriele Dominici +1
Concept-Based Models (CBMs) are a class of deep learning models that provide interpretability by explaining predictions through high-level concepts. These models first predict conc…
Neurosymbolic Object-Centric Learning with Distant Supervision
Stefano Colamonaco, David Debot, Giuseppe Marra
Neurosymbolic learning can use symbolic rules to provide supervision for latent concepts from weak labels, but it commonly assumes that the entities referenced by these rules are a…
Neural Interpretable Reasoning
Pietro Barbiero, Giuseppe Marra, Gabriele Ciravegna +5
We formalize a novel modeling framework for achieving interpretability in deep learning, anchored in the principle of inference equivariance. While the direct verification of inter…