25 citations · 73 across the 14 of their papers we have counts for
18 papers
LSEMINK: A Modified Newton-Krylov Method for Log-Sum-Exp Minimization
Kelvin Kan, James G. Nagy, Lars Ruthotto
This paper introduces LSEMINK, an effective modified Newton-Krylov algorithm geared toward minimizing the log-sum-exp function for a linear model. Problems of this kind arise commo…
Ambiguity in solving imaging inverse problems with deep learning based operators
Davide Evangelista, Elena Morotti, Elena Loli Piccolomini +1
In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well kn…
To be or not to be stable, that is the question: understanding neural networks for inverse problems
Davide Evangelista, James Nagy, Elena Morotti +1
The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem since the ill-conditioning amplifies, in the solution, the noi…
Fast deterministic approximation of symmetric indefinite kernel matrices with high dimensional datasets
Difeng Cai, James Nagy, Yuanzhe Xi
Kernel methods are used frequently in various applications of machine learning. For large-scale high dimensional applications, the success of kernel methods hinges on the ability t…
Minimizing L1 over L2 norms on the gradient
Chao Wang, Min Tao, Chen-Nee Chuah +2
In this paper, we study the L1/L2 minimization on the gradient for imaging applications. Several recent works have demonstrated that L1/L2 is better than the L1 norm when approxima…
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization
Kelvin Kan, James G Nagy, Lars Ruthotto
We demonstrate the ability of hybrid regularization methods to automatically avoid the double descent phenomenon arising in the training of random feature models (RFM). The hallmar…