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Siena Research Trip of the GAIN Group 11-18.3.
Siena Research Trip of the GAIN Group 11-18.3.
The GAIN group is pleased to visit its collaboration partners from SAILab (Siena Artificial Intelligence Lab) in Siena from 11-18 March 2022. Together with the professors Franco Scarselli and Monica Bianchi, as well as the PhD students Giuseppe Alessio D'Inverno, Veronica Lachi and Caterina Graziani, the PhD students Alice Moallemy-Oureh and Silvia Beddar-Wiesing, and GAIN group leader Josephine Thomas are working on a joint publication. The topic of the research is the extension of algorithms and associated theorems for verifying the expressiveness of graph neural networks to dynamic and attributed graphs.
News
Siena Research Trip of the GAIN Group 11-18.3.
Siena Research Trip of the GAIN Group 11-18.3.
The GAIN group is pleased to visit its collaboration partners from SAILab (Siena Artificial Intelligence Lab) in Siena from 11-18 March 2022. Together with the professors Franco Scarselli and Monica Bianchi, as well as the PhD students Giuseppe Alessio D'Inverno, Veronica Lachi and Caterina Graziani, the PhD students Alice Moallemy-Oureh and Silvia Beddar-Wiesing, and GAIN group leader Josephine Thomas are working on a joint publication. The topic of the research is the extension of algorithms and associated theorems for verifying the expressiveness of graph neural networks to dynamic and attributed graphs.
Dates
Siena Research Trip of the GAIN Group 11-18.3.
Siena Research Trip of the GAIN Group 11-18.3.
The GAIN group is pleased to visit its collaboration partners from SAILab (Siena Artificial Intelligence Lab) in Siena from 11-18 March 2022. Together with the professors Franco Scarselli and Monica Bianchi, as well as the PhD students Giuseppe Alessio D'Inverno, Veronica Lachi and Caterina Graziani, the PhD students Alice Moallemy-Oureh and Silvia Beddar-Wiesing, and GAIN group leader Josephine Thomas are working on a joint publication. The topic of the research is the extension of algorithms and associated theorems for verifying the expressiveness of graph neural networks to dynamic and attributed graphs.