Antonio De Santis — accepted AI-conference papers

2026

Learning Concept Bottleneck Models from Mechanistic Explanations

Antonio De Santis, Schrasing Tong, Marco Brambilla, Lalana Kagal

ICLR 2026poster

Concept Bottleneck Models (CBMs) aim for ante-hoc interpretability by learning a bottleneck layer that predicts interpretable concepts before the decision. State-of-the-art approaches typically select which concepts to learn via human specification, open knowledge graphs, prompting an LLM, or using…

Cited by 0 Source ↗code ↗Scholar ↗

2024

Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification

Matteo Bianchi, Antonio De Santis, Andrea Tocchetti, Marco Brambilla

IJCAI 2024poster

Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors. State-of-the-art explainability methods generate saliency maps to show where a specific class is identified, without providing a detailed explanati…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗