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Geometric Deep Learning: GNNs Beyond Permutation Equivariance 

Petar Veličković
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30 сен 2024

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Комментарии : 10   
@Fetrose
@Fetrose 8 дней назад
Very nice presentation. It is so informative, Peter.
@yephuang1401
@yephuang1401 2 года назад
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?
@nicolasgoulet4091
@nicolasgoulet4091 2 года назад
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!
@vimukthirandika872
@vimukthirandika872 7 месяцев назад
Your explanation are really good!, Thank you!
@qiguosun129
@qiguosun129 2 года назад
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.
@Janamejaya.Channegowda
@Janamejaya.Channegowda 2 года назад
Thank you for sharing.
@HelloWorlds__JTS
@HelloWorlds__JTS 9 месяцев назад
Phenomenal! Thanks, Petar!
@markadyash
@markadyash 2 года назад
GNN has arrived
@MrAstor69
@MrAstor69 2 года назад
Odlično predavanje sve pohvale.
@martinschulze5399
@martinschulze5399 Год назад
Awesome lecture, much to learn
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