36 citations · 74 across the 6 of their papers we have counts for
6 papers
eGAD! double descent is explained by Generalized Aliasing Decomposition
Mark K. Transtrum, Gus L. W. Hart, Tyler J. Jarvis +1
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is under…
Mathematical Analysis of Redistricting in Utah
Annika King, Jacob Murri, Jake Callahan +2
We discuss difficulties of evaluating partisan gerrymandering in the congressional districts in Utah and the failure of many common metrics in Utah. We explain why the Republican v…
Analysis of Normal-Form Algorithms for Solving Systems of Polynomial Equations
Suzanna Parkinson, Hayden Ringer, Kate Wall +4
We examine several of the normal-form multivariate polynomial rootfinding methods of Telen, Mourrain, and Van Barel and some variants of those methods. We analyze the performance o…
Tandem Blocks in Deep Convolutional Neural Networks
Chris Hettinger, Tanner Christensen, Jeffrey Humpherys +1
Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks. The…
Forward Thinking: Building and Training Neural Networks One Layer at a Time
Chris Hettinger, Tanner Christensen, Ben Ehlert +3
We present a general framework for training deep neural networks without backpropagation. This substantially decreases training time and also allows for construction of deep networ…
Forward Thinking: Building Deep Random Forests
Kevin Miller, Chris Hettinger, Jeffrey Humpherys +2
The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward…