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Generating Samples to Question Trained Models by Kıral, E. Mehmet; Aydın, Nurşen; Birbil, Ş. İlker is a scholarly article available to read on EtoBox.

What is Generating Samples to Question Trained Models about?

There is a growing need for investigating how machine learning models operate. With this work, we aim to understand trained machine learning models by questioning their data preferences. We propose a mathematical framework that allows us to probe trained models and identify their preferred samples in various scenarios including prediction-risky, parameter-sensitive, or model-contrastive samples. To showcase our framework, we pose these queries to a range of models trained on a range of classification and regression tasks, and receive answers in the form of generated data.

Author

Kıral, E. Mehmet; Aydın, Nurşen; Birbil, Ş. İlker

Published

2025

Language

EN