Suzanna Parkinson

According to our database, Suzanna Parkinson authored at least 5 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay.

Hannah Laus, Suzanna Parkinson, Vasileios Charisopoulos, Felix Krahmer, Rebecca Willett

CoRR, February, 2025

ReLU Neural Networks with Linear Layers Are Biased towards Single- and Multi-index Models.

Suzanna Parkinson, Greg Ongie, Rebecca Willett

SIAM J. Math. Data Sci., 2025

2024

Depth Separation in Norm-Bounded Infinite-Width Neural Networks.

Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Linear Neural Network Layers Promote Learning Single- and Multiple-Index Models.

Suzanna Parkinson, Greg Ongie, Rebecca Willett

CoRR, 2023

2022

Analysis of normal-form algorithms for solving systems of polynomial equations.

Suzanna Parkinson, Hayden Ringer, Kate Wall, Erik Parkinson, Lukas Erekson, Daniel Christensen, Tyler J. Jarvis

J. Comput. Appl. Math., 2022