2025/02/10 by Eren Mehmet Kıral, Kıral, Eren Mehmet, Nurşen Aydın +3
Computer Science · #Explainable Artificial Intelligence (XAI) #Generative Adversarial Networks and Image Synthesis #Machine Learning and Data Classification
paper · pdf · doi:10.48550/arxiv.2502.06658
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.