Critical Points of the Singular Value Decomposition

By Kevin A. O'Neil. 2005

19 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computational Theory and Mathematics, Electrical and Electronic Engineering, Mathematical Optimization, Numerical Methods & Scientific Computing

How researchers see this paper

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

In Applied Mathematics

1. Sergey Ioffe
2. Christian Szegedy
3. Yaron Lipman

In Computational Theory and Mathematics

1. Jonathan Shlomi
2. Kyle Cranmer
3. Sergey Ioffe
4. Christian Szegedy
5. Yaron Lipman
6. Haggai Maron
7. Nimrod Segol
8. Hadar Serviansky

In Electrical and Electronic Engineering

1. Kyle Cranmer
2. Sergey Ioffe
3. Christian Szegedy

In Mathematical Optimization

1. Kyle Cranmer
2. Sergey Ioffe
3. Christian Szegedy

In Numerical Methods & Scientific Computing

1. Sergey Ioffe
2. Christian Szegedy
3. Yaron Lipman

Citations in the corpus

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015, methodology): “In practice, the saturation problem and the resulting vanishing gradients are usually addressed by using Rectified Linear Units (Nair & Hinton, 2010)ReLU(x) = max(x, 0), careful initialization (Bengio & Glorot, 2010; Saxe et al., 2013), and small learning rates.”
• Set2Graph: Learning Graphs From Sets (2020, background): “Since G 2 ( X ) has a simple spectrum, U , V , Σ are all continuous in X ; Σ is unique, and U , V are unique up to a sign flip of the singular vectors (i.e., columns of U , V ) (O’Neil, 2005).”