AI for climate | Graph Neural Networks | Geometric Deep Learning | Neural Fields | Spatiotemporal Forecasting
mkofinas.github.io
The answer is simple: represent them as graphs, a.k.a. neural graphs!
Join our oral presentation and our poster at #ICLR2024 for more details.
Oral session 4B @ Halle A7 and poster #77 session 4 @ Halle B.
The answer is simple: represent them as graphs, a.k.a. neural graphs!
Join our oral presentation and our poster at #ICLR2024 for more details.
Oral session 4B @ Halle A7 and poster #77 session 4 @ Halle B.
This allows us to harness powerful graph neural networks and transformers that preserve permutation symmetry.
[5/9
This allows us to harness powerful graph neural networks and transformers that preserve permutation symmetry.
[5/9
🧪Our #ICLR2024 oral on "Graph Neural Networks for Learning Equivariant Representations of Neural Networks" answers this question!
📜:💻:🧵 [1/9
github.com/mkofinas/neura… arxiv.org/abs/2403.12143
🧪Our #ICLR2024 oral on "Graph Neural Networks for Learning Equivariant Representations of Neural Networks" answers this question!
📜:💻:🧵 [1/9
github.com/mkofinas/neura… arxiv.org/abs/2403.12143
Join us at poster session 3 on Wednesday morning, poster #619.
Paper:Source code:1/8] 🧵
github.com/mkofinas/aether arxiv.org/abs/2310.20679
Join us at poster session 3 on Wednesday morning, poster #619.
Paper:Source code:1/8] 🧵
github.com/mkofinas/aether arxiv.org/abs/2310.20679
We propose representing neural networks as computation graphs, enabling the use of standard graph neural networks to preserve permutation symmetries.
Join our poster @TAGinDS in #ICML2023!
We propose representing neural networks as computation graphs, enabling the use of standard graph neural networks to preserve permutation symmetries.
Join our poster @TAGinDS in #ICML2023!
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x.com/hyunjik11/stat…