14 citations · 14 across the 3 of their papers we have counts for
7 papers · 1 filter
Towards Identifying the Dataset Biases Causing Phantom Transfer
Jonas Jürß, Pietro Liò
Recent work has shown that a teacher model can transfer a bias to a student through a dataset from which every explicit reference to that bias has been filtered out, and that no da…
Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
Jonas Jürß, Lucie Charlotte Magister, Pietro Barbiero +2
Graph neural networks (GNNs) have led to major breakthroughs in a variety of domains such as drug discovery, social network analysis, and travel time estimation. However, they lack…
From Charts to Atlas: Merging Latent Spaces into One
Donato Crisostomi, Irene Cannistraci, Luca Moschella +4
Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces. We investigate in this study the aggregation of such…
Interpretable Graph Networks Formulate Universal Algebra Conjectures
Francesco Giannini, Stefano Fioravanti, Oguzhan Keskin +4
The rise of Artificial Intelligence (AI) recently empowered researchers to investigate hard mathematical problems which eluded traditional approaches for decades. Yet, the use of A…
SHARCS: Shared Concept Space for Explainable Multimodal Learning
Gabriele Dominici, Pietro Barbiero, Lucie Charlotte Magister +2
Multimodal learning is an essential paradigm for addressing complex real-world problems, where individual data modalities are typically insufficient to accurately solve a given mod…
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh +1
While graph neural networks (GNNs) have been shown to perform well on graph-based data from a variety of fields, they suffer from a lack of transparency and accountability, which h…