The paper maps the tiered graph autoencoder and its variational variant onto PyTorch Geometric components, and proposes a data pipeline with standard chemical identifiers.
Concept-Oriented Deep Learning: Generative Concept Representations
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abstract
Generative concept representations have three major advantages over discriminative ones: they can represent uncertainty, they support integration of learning and reasoning, and they are good for unsupervised and semi-supervised learning. We discuss probabilistic and generative deep learning, which generative concept representations are based on, and the use of variational autoencoders and generative adversarial networks for learning generative concept representations, particularly for concepts whose data are sequences, structured data or graphs.
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cs.LG 1years
2019 1verdicts
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Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
The paper maps the tiered graph autoencoder and its variational variant onto PyTorch Geometric components, and proposes a data pipeline with standard chemical identifiers.