wehub-resource-sync
17 lines
852 B
Plaintext
17 lines
852 B
Plaintext
.. _tutorials2-index:
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Batching many small graphs
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-------------------------------
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* **Tree-LSTM** `[paper] <https://arxiv.org/abs/1503.00075>`__ `[tutorial]
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<2_small_graph/3_tree-lstm.html>`__ `[PyTorch code]
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<https://github.com/dmlc/dgl/blob/master/examples/pytorch/tree_lstm>`__:
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Sentences have inherent structures that are thrown
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away by treating them simply as sequences. Tree-LSTM is a powerful model
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that learns the representation by using prior syntactic structures such as a parse-tree.
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The challenge in training is that simply by padding
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a sentence to the maximum length no longer works. Trees of different
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sentences have different sizes and topologies. DGL solves this problem by
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adding the trees to a bigger container graph, and then using message-passing
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to explore maximum parallelism. Batching is a key API for this.
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