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cs.LG2021
Learning Gradual Argumentation Frameworks using Genetic Algorithms
Jonathan Spieler, Nico Potyka, Steffen Staab
Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their graphical structure and intuitive semantics makes them a potentially interes…
cs.LG2021
Pseudo-Riemannian Graph Convolutional Networks
Bo Xiong, Shichao Zhu, Nico Potyka +3
Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry.…