Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Luzhnica, Enxhell"'
Autor:
Verdon, Guillaume, McCourt, Trevor, Luzhnica, Enxhell, Singh, Vikash, Leichenauer, Stefan, Hidary, Jack
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum syste
Externí odkaz:
http://arxiv.org/abs/1909.12264
Graph classification receives a great deal of attention from the non-Euclidean machine learning community. Recent advances in graph coarsening have enabled the training of deeper networks and produced new state-of-the-art results in many benchmark ta
Externí odkaz:
http://arxiv.org/abs/1905.04682
We propose a novel graph pooling operation using cliques as the unit pool. As this approach is purely topological, rather than featural, it is more readily interpretable, a better analogue to image coarsening than filtering or pruning techniques, and
Externí odkaz:
http://arxiv.org/abs/1904.00374
Autor:
Luzhnica, Enxhell, Kohlhase, Michael
Publikováno v:
Mathematical Software - ICMS 2016; 2016, p467-475, 9p
