Really good lecture about Geometric Deep Learning! Recently, my research paper applying GNN was questioned about the robustness of the model. This lecture gave me a lot of inspiration.
Extremely insightful! Thanks so much! Also agree that developing >1-WL GNNs using subgraphs and transfer learning (incl. pretraining) would be quite popular this year. Do you have any paper recommendations for latent graph inference?
![#71 - ZAK JOST (Graph Neural Networks + Geometric DL) [UNPLUGGED]](/img/n.gif)
Very nice presentation. It is so informative, Peter.
Your explanation are really good!, Thank you!
Really good lecture about Geometric Deep Learning! Recently, my research paper applying GNN was questioned about the robustness of the model. This lecture gave me a lot of inspiration.
Thank you so much for sharing all your work! My honours thesis will be a thing thanks to ideas I got from watching your lectures!
Phenomenal! Thanks, Petar!
Odlično predavanje sve pohvale.
Extremely insightful! Thanks so much! Also agree that developing >1-WL GNNs using subgraphs and transfer learning (incl. pretraining) would be quite popular this year. Do you have any paper recommendations for latent graph inference?
Awesome lecture, much to learn
Thank you for sharing.
GNN has arrived