3 citations · 5 across the 4 of their papers we have counts for
4 papers
How regularization affects the geometry of loss functions
Nathaniel Bottman, Y. Cooper, Antonio Lerario
What neural networks learn depends fundamentally on the geometry of the underlying loss function. We study how different regularizers affect the geometry of this function. One of t…
A comparison of group testing architectures for COVID-19 testing
J. Batson, N. Bottman, Y. Cooper +1
An important component of every country's COVID-19 response is fast and efficient testing - to identify and isolate cases, as well as for early detection of local hotspots. For man…
Gradient descent in higher codimension
Y. Cooper
We consider the behavior of gradient flow and of discrete and noisy gradient descent. It is commonly noted that the addition of noise to the process of discrete gradient descent ca…
Gradient descent in some simple settings
Y. Cooper
In this note, we observe the behavior of gradient flow and discrete and noisy gradient descent in some simple settings. It is commonly noted that addition of noise to gradient desc…