Residual Networks as Flows of Diffeomorphisms

By François Rousseau, Lucas Drumetz, Ronan Fablet. 2019

50 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability, Model Architectures

How researchers see this paper

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

In Artificial Intelligence

1. Dustin Tran
2. Balaji Lakshminarayanan
3. Shreyas Padhy
4. Tania Bedrax-Weiss
5. Zi Lin
6. Jeremiah Zhe Liu
7. David M. Blei
8. Stefano Ermon

In Statistical and Nonlinear Physics

1. Balaji Lakshminarayanan
2. Dustin Tran
3. Max Welling

In Statistics and Probability

1. Zi Lin
2. Shreyas Padhy
3. Tania Bedrax-Weiss
4. Jeremiah Zhe Liu
5. Balaji Lakshminarayanan
6. Dustin Tran
7. Stefano Ermon
8. Shakir Mohamed

In Model Architectures

1. Jeremiah Zhe Liu
2. Balaji Lakshminarayanan
3. Dustin Tran

Citations in the corpus

• Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020, methodology): “…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).”