Embedded Files
November 27, 2024
November 27, 2024
The program may change to adapt to the LoG program.
12:30 - 13:00
Welcome and introduction by the organizers
Welcome and introduction by the organizers
Ice breaking with coffee and vaflers, getting to know each other
13:00- 13:45
Tutorial on Graph neural net and molecules
Tutorial on Graph neural net and molecules
Tetiana Lutchyn and Benjamin Ricaud, UiT
Link: https://github.com/bricaud/GNNtutorial
Link: https://github.com/TLutchyn/Molecules-tutorial/LOG_meetup_tutorial_GNN_molecules.ipynb
14:30- 14:40
Break
14:40 - 15:00
Interpreting spatiotemporal GNNs
with Koopman theory
Interpreting spatiotemporal GNNs
with Koopman theory
Michele Guerra, UiT
15:00 - 15:15
Learning for type and part hierarchies
Learning for type and part hierarchies
Martin Giese , UiO
15:15- 15:30
Expressive Powers of Graph Neural Networks
Expressive Powers of Graph Neural Networks
Egor Kostylev , UiO
15:30- 15:50
Short introduction to graph embeddings and similarities
Short introduction to graph embeddings and similarities
Elisabeth Wetzer, UiT
15:50- 16:00
Break
16:00 - 16:10
Application of GNNs to molecules and proteins
Application of GNNs to molecules and proteins
Yannick Luca Kiefl, UiT
16:10 - 16:20
Visualisation of the LLM latent space with graphs
Visualisation of the LLM latent space with graphs
Sebastian Tendstrand Iversen, UiT
16:20 - 17:00
Discussion of the LoG meetup
Discussion of the LoG meetup
Pizzas and discussion
17:00 - 18:00
LoG conference keynote
LoG conference keynote
Yusu Wang (UCSD) keynote or some talks (tbc)
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