wehub-resource-sync
15 lines
681 B
Plaintext
15 lines
681 B
Plaintext
.. _tutorials3-index:
|
|
|
|
Generative models
|
|
--------------------
|
|
|
|
* **DGMG** `[paper] <https://arxiv.org/abs/1803.03324>`__ `[tutorial]
|
|
<3_generative_model/5_dgmg.html>`__ `[PyTorch code]
|
|
<https://github.com/dmlc/dgl/tree/master/examples/pytorch/dgmg>`__:
|
|
This model belongs to the family that deals with structural
|
|
generation. Deep generative models of graphs (DGMG) uses a state-machine approach.
|
|
It is also very challenging because, unlike Tree-LSTM, every
|
|
sample has a dynamic, probability-driven structure that is not available
|
|
before training. You can progressively leverage intra- and
|
|
inter-graph parallelism to steadily improve the performance.
|