Title of Abstract: The Heavy-Tailed Nature of Noise in Training Neural Networks
Name of Mentor: Thomas Pietraho
Mentor’s Organization or Department: Department of Mathematics, Bowdoin College
Research Abstract: Artificial neural networks are machine-learning algorithms used to forecast events and optimize systems in a wide scope of areas including technology, finance, and mechanics. We analyzed the randomness that occurs in the machine-learning optimization process and found that the optimization process is non-Gaussian. Additionally, we found that the larger the neural network, the less Gaussian and more risky the optimization process behaves. Our findings provide insight for how to create better neural networks.
Nora Jackson ’21