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Found 2 papers, 1 papers with code
Decoupling feature propagation from the design of graph auto-encoders
We present two instances, L-GAE and L-VGAE, of the variational graph auto-encoding family (VGAE) based on separating feature propagation operations from graph convolution layers typically found in graph learning methods to a single linear matrix computation made prior to input in standard auto-encoder architectures.
Factorised Neural Relational Inference for Multi-Interaction Systems
Many complex natural and cultural phenomena are well modelled by systems of simple interactions between particles.
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