Geometric Deep Learning

On the Expressive Power of Geometric Graph Neural Networks
We propose a Geometric Weisfeiler-Leman test to study the expressive power of Geometric Graph Neural Networks and demonstrate the advantages of equivariant GNNs over invariant GNNs. (Full paper at ICML 2023, and presented as an Oral at NeurIPS 2022 Workshop)
Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò
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On the Expressive Power of Geometric Graph Neural Networks
Multi-State RNA Design with Geometric Multi-Graph Neural Networks
We propose gRNAde, a geometric deep learning pipeline for 3D RNA inverse design conditioned on multiple backbone conformations. (ICML 2023 Workshop on Computational Biology)
Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon Mathis, Pietro Liò
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Multi-State RNA Design with Geometric Multi-Graph Neural Networks