5 papers · 1 filter
Reducing Symmetry Increase in Equivariant Neural Networks
Ning Lin, Jiacheng Cen, Anyi Li +2
Equivariant Neural Networks (ENNs) have empowered numerous applications in scientific fields. Despite their remarkable capacity for representing geometric structures, ENNs suffer f…
Planar Symmetric Pattern Generation
Ning Lin, Luxi Chen, Huaguan Chen +4
Generating objects with specific symmetries is essential in various real-world scenarios. However, adapting existing 2D continuous representations to enforce planar group symmetry…
Universally Invariant Learning in Equivariant GNNs
Jiacheng Cen, Anyi Li, Ning Lin +5
Equivariant Graph Neural Networks (GNNs) have demonstrated significant success across various applications. To achieve completeness -- that is, the universal approximation property…
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
Jiacheng Cen, Anyi Li, Ning Lin +3
Equivariant Graph Neural Networks (GNNs) that incorporate E(3) symmetry have achieved significant success in various scientific applications. As one of the most successful models,…
Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
Zeyang Zhang, Xingwang Li, Fei Teng +4
Human albumin is essential for indicating the body's overall health. Accurately predicting plasma albumin levels and determining appropriate doses are urgent clinical challenges, p…