Deriving Neural Architectures from Sequence and Graph Kernels

ICML, pp. 2024-2033, 2017.

Cited by: 75|Bibtex|Views42|Links
EI

Abstract:

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

Code:

Data:

Your rating :
0

 

Tags
Comments