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Found 5 papers, 1 papers with code
Equivariant Networks for Hierarchical Structures
While using invariant and equivariant maps, it is possible to apply deep learning to a range of primitive data structures, a formalism for dealing with hierarchy is lacking.
Out-of-Sample Representation Learning for Knowledge Graphs
Many important problems can be formulated as reasoning in knowledge graphs.
Equivariant Maps for Hierarchical Structures
While using invariant and equivariant maps, it is possible to apply deep learning to a range of primitive data structures, a formalism for dealing with hierarchy is lacking.
Out-of-Sample Representation Learning for Multi-Relational Graphs
Many important problems can be formulated as reasoning in knowledge graphs.
Incidence Networks for Geometric Deep Learning
Sparse incidence tensors can represent a variety of structured data.
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