61 citations · 358 across the 40 of their papers we have counts for
4 papers · 1 filter
Three proofs of the Benedetto-Fickus theorem
Dustin G. Mixon, Tom Needham, Clayton Shonkwiler +1
In 2003, Benedetto and Fickus introduced a vivid intuition for an objective function called the frame potential, whose global minimizers are fundamental objects known today as unit…
A simple equivariant machine learning method for dynamics based on scalars
Weichi Yao, Kate Storey-Fisher, David W. Hogg +1
Physical systems obey strict symmetry principles. We expect that machine learning methods that intrinsically respect these symmetries should have higher prediction accuracy and bet…
Scalars are universal: Equivariant machine learning, structured like classical physics
Soledad Villar, David W. Hogg, Kate Storey-Fisher +2
There has been enormous progress in the last few years in designing neural networks that respect the fundamental symmetries and coordinate freedoms of physical law. Some of these f…
Fitting very flexible models: Linear regression with large numbers of parameters
David W. Hogg, Soledad Villar
There are many uses for linear fitting; the context here is interpolation and denoising of data, as when you have calibration data and you want to fit a smooth, flexible function t…