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Found 4 papers, 4 papers with code
Learning Symmetrization for Equivariance with Orbit Distance Minimization
We present a general framework for symmetrizing an arbitrary neural-network architecture and making it equivariant with respect to a given group.
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
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)
Pure Transformers are Powerful Graph Learners
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
bertha: Project Skeleton for Scientific Software
Science depends heavily on reliable and easy-to-use software packages, such as mathematical libraries or data analysis tools.
Mathematical Software
