4 citations · 9 across the 6 of their papers we have counts for
6 papers
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
John T. Halloran, David M. Rocke
One of the most efficient methods to solve L2-regularized primal problems, such as logistic regression and linear support vector machine (SVM) classification, is the widely used tr…
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra
John T. Halloran, David M. Rocke
The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra re…
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra
John T. Halloran, David M. Rocke
Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is ge…
Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization
Rishabh Iyer, John T. Halloran, Kai Wei
This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a framework of convex (or l…
Faster graphical model identification of tandem mass spectra using peptide word lattices
Shengjie Wang, John T. Halloran, Jeff A. Bilmes +1
Liquid chromatography coupled with tandem mass spectrometry, also known as shotgun proteomics, is a widely-used high-throughput technology for identifying proteins in complex biolo…
Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra
Ajit P. Singh, John Halloran, Jeff A. Bilmes +2
Shotgun proteomics is a high-throughput technology used to identify unknown proteins in a complex mixture. At the heart of this process is a prediction task, the spectrum identific…