1 code implementation • 13 Nov 2023 • Tien Dat Nguyen, Jinwoo Kim, Hongseok Yang, Seunghoon Hong
We present a general framework for symmetrizing an arbitrary neural-network architecture and making it equivariant with respect to a given group.
1 code implementation • NeurIPS 2023 • Jinwoo Kim, Tien Dat Nguyen, Ayhan Suleymanzade, Hyeokjun An, Seunghoon Hong
In contrary to equivariant architectures, we use an arbitrary base model such as an MLP or a transformer and symmetrize it to be equivariant to the given group by employing a small equivariant network that parameterizes the probabilistic distribution underlying the symmetrization.
Ranked #1 on Link Prediction on PCQM-Contact (using extra training data)
1 code implementation • 6 Jul 2022 • Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice.
Ranked #15 on Graph Regression on PCQM4Mv2-LSC
2 code implementations • 3 Dec 2019 • Michael Riesch, Tien Dat Nguyen, Christian Jirauschek
Science depends heavily on reliable and easy-to-use software packages, such as mathematical libraries or data analysis tools.
Mathematical Software