The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.

May 25, 2017 · In this work, we appeal to kernels over combinatorial structures, such as sequences and graphs, to derive appropriate neural operations. We ...

In this work, we appeal to kernels over combinatorial struc- tures, such as sequences and graphs, to derive appropriate neural operations. We introduce a class ...

The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.

This repo contains the code of the paper: Deriving Neural Architectures from Sequence and Graph Kernels. ICML 2017. [PDF]

The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.

Jun 14, 2017 · The design of neural architectures for structured objects is typically guidedby experimental insights rather than a formal process.

May 30, 2019 · Deriving neural architectures from sequence and graph kernels. In Proceedings of the 34 th In- ternational Conference on Machine Learning ...

In this paper we propose a neural architecture that bridges the two worlds of graph kernels and ... Deriving neural architectures from sequence and graph kernels.

This work adapts the neural encoding for sequence kernels by replacing the exact match between subsequences with a gaussian kernel accounting for vectorial ...