Complexity Science Hub Vienna CSH

Projekt → Error-correcting Codes and Computation Error-correcting codes are a method for a redundant representation of data, that enables one to can recover the original data eve… Beitrag ansehen → Projekt → The Potential of Artificial Intelligence as the Learning Ecosystem of the Future in Adult Religious Education In today's information society, the rapid pace of technological advancements and the increasing complexity of information process… Beitrag ansehen → Projekt → QuXAI: Explainers for Hybrid Quantum Machine Learning Models The emergence of hybrid quantum-classical machine learning (HQML) models opens new horizons of computational intelligence but the… Beitrag ansehen → Projekt → Validation of a Machine Learning Model for Certification Using Symbolic Regression and a Behaviour Envelope Aviation is highly regulated with a strong focus on safety. If machine learning models are to be used in aviation, their correctn… Beitrag ansehen → Projekt → Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating g… Beitrag ansehen → Projekt → Evaluation of Explainable Artificial Intelligence for Predictive Process Mining in Education Process mining leverages event log data to extract valuable knowledge and insights about the underlying processes. Education has… Beitrag ansehen →