19 citations · 35 across the 11 of their papers we have counts for
8 papers · 1 filter
DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework
Pooja Gupta, Jyoti Maggu, Angshul Majumdar +2
This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well ac…
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
Pooja Gupta, Jyoti Maggu, Angshul Majumdar +2
This work addresses the problem of analyzing multi-channel time series data %. In this paper, we by proposing an unsupervised fusion framework based on %the recently proposed convo…
Deep Convolutional Transform Learning -- Extended version
Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux +1
This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL). By stacking convolutional transforms, our approach is…
A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials
Aanchal Mongia, Sanjay Kr. Saha, Emilie Chouzenoux +1
COVID-19 has fast-paced drug re-positioning for its treatment. This work builds computational models for the same. The aim is to assist clinicians with a tool for selecting prospec…
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks
Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet +1
On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of researc…
Block Distributed Majorize-Minimize Memory Gradient Algorithm and its application to 3D image restoration
Mathieu Chalvidal, Emilie Chouzenoux
Modern 3D image recovery problems require powerful optimization frameworks to handle high dimensionality while providing reliable numerical solutions in a reasonable time. In this…