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Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models
Art der Publikation: Artikel in einem Konferenzbericht
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Buchtitel: Post-proceedings of the 6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems
Jahr: 2024
Monat: August
Abriss: Deep Learning (DL) models are increasingly dealing with heterogeneous data (i.e., a mix of structured and unstructured data), calling for adequate eXplainable Artificial Intelligence (XAI) methods. Nevertheless, only some of the existing techniques consider the uncer- tainty inherent to the data. To this end, this study proposes a pipeline to explain heterogeneous data-based DL models by combining embed- ding analysis, rule extraction methods, and probabilistic models. The proposed pipeline has been tested using synthetic data (multi-individual food items tracking). This study has achieved (i) inference enhancement through probabilistic and evidential reasoning, (ii) generation of logical explanations based on extracted rules and predictions, and (iii) integra- tion of textual data into the explanation pipeline through embedding analysis.
Schlagworte: Deep Learning, Heterogeneous data processing, Preference modeling, rule extraction, Uncertainty reasoning, XAI
Autoren Contreras, Victor H.
Schumacher, Michael
Calvaresi, Davide
Hinzugefügt von: []
Gesamtbewertung: 0
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