1 code implementation • 28 Sep 2021 • Elizabeth Newman, Julianne Chung, Matthias Chung, Lars Ruthotto
In the absence of theoretical guidelines or prior experience on similar tasks, this requires solving many training problems, which can be time-consuming and demanding on computational resources.
1 code implementation • 26 Jul 2020 • Elizabeth Newman, Lars Ruthotto, Joseph Hart, Bart van Bloemen Waanders
To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems.
no code implementations • 25 Sep 2019 • Elizabeth Newman, Misha E. Kilmer
Building on that work, in this paper, we use of non-negative tensor patch-based dictionaries trained on other data, such as facial image data, for the purposes of either compression or image deblurring.
no code implementations • 15 Nov 2018 • Elizabeth Newman, Lior Horesh, Haim Avron, Misha Kilmer
To exemplify the elegant, matrix-mimetic algebraic structure of our $t$-NNs, we expand on recent work (Haber and Ruthotto, 2017) which interprets deep neural networks as discretizations of non-linear differential equations and introduces stable neural networks which promote superior generalization.
no code implementations • 29 Jun 2017 • Elizabeth Newman, Misha Kilmer, Lior Horesh
From linear classifiers to neural networks, image classification has been a widely explored topic in mathematics, and many algorithms have proven to be effective classifiers.