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Author: Keith

Feature-level Malware Obfuscation in Deep Learning

We consider the problem of detecting malware with deep learning models, where the malware may be combined with significant amounts of benign code. Examples of this include piggybacking and trojan horse attacks on a system, where malicious behavior is hidden

Keith February 10, 2020May 15, 2023 Preprint Read more

Quadratic Programming with Keras

This note describes how to implement and solve a quadratic programming optimization problem using a shallow neural network in Keras. A single linear layer is used with a custom one-sided loss to impose the inequality constraints. A custom kernel regularizer

Keith December 29, 2019May 15, 2023 Report Read more

Clustering Gaussian Graphical Models

We derive an efficient method to perform clustering of nodes in Gaussian graphical models directly from sample data. Nodes are clustered based on the similarity of their network neighborhoods, with edge weights defined by partial correlations. In the limited-data scenario,

Keith October 5, 2019May 15, 2023 Preprint Read more

On the Computation and Applications of Large Dense Partial Correlation Networks

While sparse inverse covariance matrices are very popular for modeling network connectivity, the value of the dense solution is often overlooked. In fact the L2-regularized solution has deep connections to a number of important applications to spectral graph theory, dimensionality

Keith March 17, 2019May 15, 2023 Preprint Read more

Spectral Resolution Clustering for Brain Parcellation

We take an image science perspective on the problem of determining brain network connectivity given functional activity. But adapting the concept of image resolution to this problem, we provide a new perspective on network partitioning for individual brain parcellation. The

Keith October 7, 2018May 15, 2023 Preprint Read more

A regularized clustering approach to brain parcellation from functional MRI data

We consider a data-driven approach for the subdivision of an individual subject’s functional Magnetic Resonance Imaging (fMRI) scan into regions of interest, i.e., brain parcellation. The approach is based on a computational technique for calculating resolution from inverse problem theory,

Keith September 6, 2017May 15, 2023 Conference Read more

A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI

Our goal is to identify the brain regions most relevant to mental illness using neuroimaging. State of the art machine learning methods commonly suffer from repeatability difficulties in this application, particularly when using large and heterogeneous populations for samples. We

Keith January 1, 2017May 15, 2023 Journal Read more

Fast and robust estimation of ophthalmic wavefront aberrations

Rapidly rising levels of myopia, particularly in the developing world, have led to an increased need for inexpensive and automated approaches to optometry. A simple and robust technique is provided for estimating major ophthalmic aberrations using a gradient-based wavefront sensor.

Keith December 1, 2016May 15, 2023 Journal Read more

Computational estimation of resolution in reconstruction techniques utilizing sparsity, total variation, and nonnegativity

Techniques which exploit properties such as sparsity and total variation have provided the ability to reconstruct images that surpass the conventional limits of imaging. This leads to difficulties in assessing the result, as conventional metrics for resolution are no longer

Keith July 1, 2016May 15, 2023 Journal Read more

Imposing uniqueness to achieve sparsity

In this paper we take a novel approach to the regularization of underdetermined linear systems. Typically, a prior distribution is imposed on the unknown to hopefully force a sparse solution, which often relies on uniqueness of the regularized solution (something

Keith June 1, 2016May 15, 2023 Journal Read more
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