Principles of Riemannian Geometry in Neural Networks

By Michael Hauser, Asok Ray. 2017

77 citations recorded by the source corpus.

Subfields: Statistical Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical Learning Theory

1. Shreyas Padhy
2. François Rousseau
3. Ronan Fablet
4. Lucas Drumetz
5. Balaji Lakshminarayanan
6. Dustin Tran

Citations in the corpus

• Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020, background): “…spaces X and H is well-established in the learning theory and the generative modeling literature, but the application of these results in the context of uncertainty estimation for DNN appear to be new (Bartlett et al., 2018; Behrmann et al., 2019; Hauser and Ray, 2017; Rousseau et al., 2020).”
• Residual Networks as Flows of Diffeomorphisms (2019, methodology): “This interpretation is also pointed out in [13] and discussed in terms of Riemannian geometry.”