Deep networks are framed as memory spaces whose complexity is defined by a Fisher metric, with the least action principle linking this complexity to generalization and disentanglement for better interpretability.
Understanding over-parameterized deep networks by geometrization
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
A complete understanding of the widely used over-parameterized deep networks is a key step for AI. In this work we try to give a geometric picture of over-parameterized deep networks using our geometrization scheme. We show that the Riemannian geometry of network complexity plays a key role in understanding the basic properties of over-parameterizaed deep networks, including the generalization, convergence and parameter sensitivity. We also point out deep networks share lots of similarities with quantum computation systems. This can be regarded as a strong support of our proposal that geometrization is not only the bible for physics, it is also the key idea to understand deep learning systems.
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Deep network as memory space: complexity, generalization, disentangled representation and interpretability
Deep networks are framed as memory spaces whose complexity is defined by a Fisher metric, with the least action principle linking this complexity to generalization and disentanglement for better interpretability.