We express the equivariant map for the hierarchy using an intuitive combination of the equivariant linear layers of the building blocks.
This paper presents a procedure to design neural networks equivariant to hierarchical symmetries and nested structures. We describe how the imprimitive ...
Summary and Contributions: The paper tackles the problem of learning hierarchical structures (for example set of sequences, image of images ) by equivariant ...
NeurIPS 2020. Equivariant Networks for Hierarchical Structures. Meta Review. The paper attempts to improve invariance/equivariance modelling in neural network.
Jun 5, 2020 · We express the equivariant map for the hierarchy using an intuitive combination of the equivariant linear layers of the building blocks.
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Equivariant Graph Hierarchy-Based Neural Networks (EGHNs) are novel graph networks that incorporate automatic hierarchical modeling into equivariant GNNs.
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Feb 22, 2022 · Abstract:Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems.
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Oct 31, 2022 · Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems.
This hierarchical, equivariant network architecture enables us to learn directly from a complete atomic repre- sentation of the protein complex, without making ...
Equivariant Networks for Hierarchical Structures. Wang, R., Albooyeh, M ... Equivariant Networks for Hierarchical Structures [pdf] Pdf Equivariant ...