2023 Edition

• 14h00 – 14h15: Introduction 

• 14h15 – 15h00: Xavier Bresson (NUS) *
  "GNN overview, applications and Graph transformer/ViT/MLP-Mixer". 

• 15h00 – 15h15: Dominic Masters (Graphcore)
  "Molecular Machine Learning at Scale"

• 15h15 – 15h30: Johannes Lutzeyer (Polytechnique)
  "Message Passing In Graph Neural Networks"

• 15-min break

• 15h45 – 16h00: Michaël Defferrarad (Qualcomm)
  "Leveraging topology, geometry, and symmetries for efficient Machine Learning"

• 16h00 – 16h15: Vassilis Ioannidis (Amazon) *
  "LM-GNN: Fusing text and graph structure at scale"

• 16h15 –17h00: Michael Bronstein (Oxford) *
  "Physics inspired GNNs"

• 17h – 18h/19h: Poster session and socials
  
  

* denotes virtual speakers. 

• A GNN Approach for Cell-Free Massive MIMO, Lou Salaun, Hong Yang, Shashwat Mishra and Chung Shue Chen, Nokia Bell labs. 

• Deep learning of the relationship between 3D structure and chemical properties of proteins depending on their representation; Helene Bret, Jessica Andreani and Raphael Guerois

• Learning with Combinatorial Optimization Layers: a Probabilistic Approach, Guillaume Dalle, Léo Baty, Louis Bouvier and Axel Parmentier.

• Graph Ordering Attention Networks, Michail Chatzianastasis

• Geometric Random Walk Graph Neural Networks via Implicit Layers, Giannis Nikolentzos, Michalis Vazirgiannis

• Exploration of redox properties in chemical space with GNNs;  Rostislav Fedorov, Anastasiia Nihei and Ganna Gryn'Ova. 

• Maximizing Influence with Graph Neural Networks; George Panagopoulos, Nikolaos Tziortziotis, Fragkiskos D. Malliaros and Michalis Vazirgiannis

• GPS++: Reviving the Art of Message Passing for Molecular Property Prediction; Dominic Masters. 

• Limitless Stability for Graph Convolutional Networks, Christian Koke

• FAENet: Frame Averaging Equivariant GNN for Material Modelling; Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio and David Rolnick.