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.
Geometrization of deep networks for the interpretability of deep learning systems
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abstract
How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical world. By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.
fields
cs.LG 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Authors propose a fibre bundle gauge theory model for disentangled representations and connect it to the relativity twins paradox.
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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.
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Gauge theory and twins paradox of disentangled representations
Authors propose a fibre bundle gauge theory model for disentangled representations and connect it to the relativity twins paradox.