2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Designing with Non-Finite Output Dimension via Fourier Coefficients of Neural Waveforms
Jonathan S. Kent
Ordinary Deep Learning models require having the dimension of their outputs determined by a human practitioner prior to training and operation. For design tasks, this places a hard…
cs.LG2021
DOODLER: Determining Out-Of-Distribution Likelihood from Encoder Reconstructions
Jonathan S. Kent, Bo Li
Deep Learning models possess two key traits that, in combination, make their use in the real world a risky prospect. One, they do not typically generalize well outside of the distr…
cs.CV2021★ 2 cited
Unsupervised Learning for Target Tracking and Background Subtraction in Satellite Imagery
Jonathan S. Kent, Charles C. Wamsley, Davin Flateau +1
This paper describes an unsupervised machine learning methodology capable of target tracking and background suppression via a novel dual-model approach. ``Jekyll`` produces a video…