Deriving Neural Architectures from Sequence and Graph Kernels
ICML, pp. 2024-2033, 2017.
The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process. In this work, we appeal to kernels over combinatorial structures, such as sequences and graphs, to derive appropriate neural operations. We introduce a class of deep recurrent neural operations and formally ...More
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