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Found 5 papers, 1 papers with code
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
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 trust region Newton algorithm, TRON.
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra
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 representative of the peptides (i. e., protein subsequences) present in the original sample.
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra
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.
Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization
This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization.
Faster graphical model identification of tandem mass spectra using peptide word lattices
Liquid chromatography coupled with tandem mass spectrometry, also known as shotgun proteomics, is a widely-used high-throughput technology for identifying proteins in complex biological samples.
