Deriving neural architectures from sequence and graph kernels
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 r...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
MLResearch Press
2021
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| Online Access: | https://hdl.handle.net/1721.1/130480 |