no code implementations • 8 Oct 2021 • Lorenzo Luzi, Carlos Ortiz Marrero, Nile Wynar, Richard G. Baraniuk, Michael J. Henry
We define a performance measure, which we call WaM, on two sets of images by using Inception-v3 (or another classifier) to featurize the images, estimate two GMMs, and use the restricted $2$-Wasserstein distance to compare the GMMs.
no code implementations • 9 Oct 2018 • Craig Bakker, Michael J. Henry, Nathan O. Hodas
In this paper, we show that feedforward and recurrent neural networks exhibit an outer product derivative structure but that convolutional neural networks do not.
no code implementations • ICLR 2018 • Craig Bakker, Michael J. Henry, Nathan O. Hodas
Training methods for deep networks are primarily variants on stochastic gradient descent.
no code implementations • 26 Dec 2017 • Jesse M. Johns, Jeremiah Rounds, Michael J. Henry
Estimating the location where an image was taken based solely on the contents of the image is a challenging task, even for humans, as properly labeling an image in such a fashion relies heavily on contextual information, and is not as simple as identifying a single object in the image.