dmlc--dgl
Minjie Wang
8a07ab7737
* reorg * change titles * rm some stale API doc; minor fix * fix docs * add warning * rm new-tutorial run in ci * lint
15 行
681 B
Plaintext
15 行
681 B
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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