7 citations · 7 across the 4 of their papers we have counts for
4 papers
Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
Hannah Laus, Suzanna Parkinson, Vasileios Charisopoulos +2
Machine learning methods are commonly used to solve inverse problems, wherein an unknown signal must be estimated from few indirect measurements generated via a known acquisition p…
Depth Separation in Norm-Bounded Infinite-Width Neural Networks
Suzanna Parkinson, Greg Ongie, Rebecca Willett +2
We study depth separation in infinite-width neural networks, where complexity is controlled by the overall squared -norm of the weights (sum of squares of all weights in th…
ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models
Suzanna Parkinson, Greg Ongie, Rebecca Willett
Neural networks often operate in the overparameterized regime, in which there are far more parameters than training samples, allowing the training data to be fit perfectly. That is…
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…