34 citations · 57 across the 7 of their papers we have counts for
3 papers · 1 filter
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…
Dynamic estimation with random forests for discrete-time survival data
Hoora Moradian, Weichi Yao, Denis Larocque +2
Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available. In this paper, we in…