11 citations · 15 across the 10 of their papers we have counts for
4 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…
Wasserstein-based Graph Alignment
Hermina Petric Maretic, Mireille El Gheche, Matthias Minder +2
We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph…