Function space analysis of deep learning representation layers

Explore this paper's citation graph

Summary

A function space approach to Representation Learning and the analysis of the representation layers in deep learning architectures is proposed and a weak-type Besov smoothness index is computed that quantifies the geometry of the clustering in the feature space.

Type
preprint
Published
2017-10-09
Cited by
4
References
44
Access
Open access

Keywords

Smoothness, Gradient boosting, Representation (politics), Computer science, Artificial intelligence

References

Cited by

Related papers