20 citations · 40 across the 12 of their papers we have counts for
4 papers · 1 filter
FrED: External Data Influence Estimation via Domain Knowledge Graph Grounding
Theodoros Aivalis, Iraklis A. Klampanos, Antonis Troumpoukis +1
The rapid deployment of generative AI has amplified the critical need for Training Data Attribution to ensure transparency and accountability. However, current parametric approache…
Training Data Attribution for Image Generation using Ontology-Aligned Knowledge Graphs
Theodoros Aivalis, Iraklis A. Klampanos, Antonis Troumpoukis +1
As generative models become powerful, concerns around transparency, accountability, and copyright violations have intensified. Understanding how specific training data contributes…
Enhancing Interpretability in Generative AI Through Search-Based Data Influence Analysis
Theodoros Aivalis, Iraklis A. Klampanos, Antonis Troumpoukis +1
Generative AI models offer powerful capabilities but often lack transparency, making it difficult to interpret their output. This is critical in cases involving artistic or copyrig…
ANNETT-O: An Ontology for Describing Artificial Neural Network Evaluation, Topology and Training
Iraklis A. Klampanos, Athanasios Davvetas, Antonis Koukourikos +1
Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and n…