dmlc--dgl
John Andrilla
22 行
1.1 KiB
Plaintext
22 行
1.1 KiB
Plaintext
.. _tutorials3-index:
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Generative models
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==================
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* **DGMG** `[paper] <https://arxiv.org/abs/1803.03324>`__ `[tutorial]
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<3_generative_model/5_dgmg.html>`__ `[PyTorch code]
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<https://github.com/dmlc/dgl/tree/master/examples/pytorch/dgmg>`__:
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This model belongs to the family that deals with structural
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generation. Deep generative models of graphs (DGMG) uses a state-machine approach.
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It is also very challenging because, unlike Tree-LSTM, every
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sample has a dynamic, probability-driven structure that is not available
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before training. You can progressively leverage intra- and
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inter-graph parallelism to steadily improve the performance.
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* **JTNN** `[paper] <https://arxiv.org/abs/1802.04364>`__ `[PyTorch code]
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<https://github.com/dmlc/dgl/tree/master/examples/pytorch/jtnn>`__:
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This network generates molecular graphs using the framework of
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a variational auto-encoder. The junction tree neural network (JTNN) builds
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structure hierarchically. In the case of molecular graphs, it uses a junction tree as
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the middle scaffolding.
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