19 citations · 35 across the 11 of their papers we have counts for
8 papers · 1 filter
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux +2
Standard few-shot benchmarks are often built upon simplifying assumptions on the query sets, which may not always hold in practice. In particular, for each task at testing time, th…
Deep Transform and Metric Learning Networks
Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet +1
Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest. Wh…
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…
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…