Learnable Commutative Monoids for Graph Neural Networks

16 December 2022

Papers citing "Learnable Commutative Monoids for Graph Neural Networks"

11 / 11 papers shown

Title
What makes a good feedforward computational graph? Alex Vitvitskyi J. G. Araújo Marc Lackenby Petar Velickovic 85 1 0 10 Feb 2025
softmax is not enough (for sharp out-of-distribution) Petar Veličković Christos Perivolaropoulos Federico Barbero Razvan Pascanu 39 18 0 01 Oct 2024
Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks Mitchell Keren Taraday Almog David Chaim Baskin 28 0 0 28 Sep 2024
Probabilistic Invariant Learning with Randomized Linear Classifiers Leonardo Cotta Gal Yehuda Assaf Schuster Chris J. Maddison 29 2 0 08 Aug 2023
Asynchronous Algorithmic Alignment with Cocycles Andrew Dudzik Tamara von Glehn Razvan Pascanu Petar Velivcković 26 9 0 27 Jun 2023
How does over-squashing affect the power of GNNs? Francesco Di Giovanni T. Konstantin Rusch Michael M. Bronstein Andreea Deac Marc Lackenby Siddhartha Mishra Petar Velivcković 30 34 0 06 Jun 2023
Categorical Foundations of Explainable AI: A Unifying Theory Pietro Barbiero S. Fioravanti Francesco Giannini Alberto Tonda Pietro Lio' Elena Di Lavore XAI 24 2 0 27 Apr 2023
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 174 1,106 0 27 Apr 2021
Combinatorial optimization and reasoning with graph neural networks Quentin Cappart Didier Chételat Elias Boutros Khalil Andrea Lodi Christopher Morris Petar Velickovic AI4CE 32 347 0 18 Feb 2021
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 189 916 0 02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 267 1,945 0 09 Jun 2018