38 citations · 55 across the 7 of their papers we have counts for
10 papers
Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks
Hanzhi Yu, Hasan Farooq, Julien Forgeat +4
In this paper, we investigate a novel digital network twin (DNT) assisted deep learning (DL) model training framework. In particular, we consider a physical network where a base st…
Change in Quantitative Bipolar Argumentation: Sufficient, Necessary, and Counterfactual Explanations
Timotheus Kampik, Kristijonas Čyras, José Ruiz Alarcón
This paper presents a formal approach to explaining change of inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). When drawing conclusions from a QBAF and updating…
Multi-Task Learning as enabler for General-Purpose AI-native RAN
Hasan Farooq, Julien Forgeat, Shruti Bothe +2
The realization of data-driven AI-native architecture envisioned for 6G and beyond networks can eventually lead to multiple machine learning (ML) workloads distributed at the netwo…
Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis
Timotheus Kampik, Nico Potyka, Xiang Yin +2
We present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another. The introdu…
Machine Reasoning Explainability
Kristijonas Cyras, Ramamurthy Badrinath, Swarup Kumar Mohalik +5
As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainabl…
Complexity Results and Algorithms for Bipolar Argumentation
Amin Karamlou, Kristijonas Čyras, Francesca Toni
Bipolar Argumentation Frameworks (BAFs) admit several interpretations of the support relation and diverging definitions of semantics. Recently, several classes of BAFs have been ca…