Search Results for author: Joshua Vendrow

Found 8 papers, 5 papers with code

A Generalized Hierarchical Nonnegative Tensor Decomposition

1 code implementation30 Sep 2021 Joshua Vendrow, Jamie Haddock, Deanna Needell

Hierarchical NTF (HNTF) methods have been proposed, however these methods do not naturally generalize their matrix-based counterparts.

Tensor Decomposition

Analysis of Legal Documents via Non-negative Matrix Factorization Methods

no code implementations28 Apr 2021 Ryan Budahazy, Lu Cheng, Yihuan Huang, Andrew Johnson, Pengyu Li, Joshua Vendrow, Zhoutong Wu, Denali Molitor, Elizaveta Rebrova, Deanna Needell

The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files.

Learning low-rank latent mesoscale structures in networks

2 code implementations13 Feb 2021 Hanbaek Lyu, Yacoub H. Kureh, Joshua Vendrow, Mason A. Porter

It is common to use networks to encode the architecture of interactions between entities in complex systems in applications in the physical, biological, social, and information sciences.

Denoising Dictionary Learning

Neural Nonnegative CP Decomposition for Hierarchical Tensor Analysis

no code implementations1 Jan 2021 Joshua Vendrow, Jamie Haddock, Deanna Needell

We propose a new hierarchical nonnegative CANDECOMP/PARAFAC (CP) decomposition (hierarchical NCPD) model and a training method, Neural NCPD, for performing hierarchical topic modeling on multi-modal tensor data.

Document Classification

Learning to predict synchronization of coupled oscillators on randomly generated graphs

1 code implementation28 Dec 2020 Hardeep Bassi, Richard Yim, Rohith Kodukula, Joshua Vendrow, Cherlin Zhu, Hanbaek Lyu

However, in the problem setting where these graph statistics cannot distinguish the two classes very well (e. g., when the graphs are generated from the same random graph model), we find that pairing a few iterations of the initial dynamics along with the graph statistics as the input to our classification algorithms can lead to significant improvement in accuracy; far exceeding what is known by the classical oscillator theory.

On a Guided Nonnegative Matrix Factorization

1 code implementation22 Oct 2020 Joshua Vendrow, Jamie Haddock, Elizaveta Rebrova, Deanna Needell

Fully unsupervised topic models have found fantastic success in document clustering and classification.

Topic Models

Feature Selection on Lyme Disease Patient Survey Data

no code implementations24 Aug 2020 Joshua Vendrow, Jamie Haddock, Deanna Needell, Lorraine Johnson

We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.