116 citations · 329 across the 40 of their papers we have counts for
6 papers · 1 filter
Multi-layered tensor networks for image classification
Raghavendra Selvan, Silas Ørting, Erik B Dam
The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patc…
Locally orderless tensor networks for classifying two- and three-dimensional medical images
Raghavendra Selvan, Silas Ørting, Erik B Dam
Tensor networks are factorisations of high rank tensors into networks of lower rank tensors and have primarily been used to analyse quantum many-body problems. Tensor networks have…
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
Lasse F. Wolff Anthony, Benjamin Kanding, Raghavendra Selvan
Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware…
Uncertainty quantification in medical image segmentation with normalizing flows
Raghavendra Selvan, Frederik Faye, Jon Middleton +1
Medical image segmentation is inherently an ambiguous task due to factors such as partial volumes and variations in anatomical definitions. While in most cases the segmentation unc…
Lung Segmentation from Chest X-rays using Variational Data Imputation
Raghavendra Selvan, Erik B. Dam, Nicki S. Detlefsen +4
Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with suc…
Tensor Networks for Medical Image Classification
Raghavendra Selvan, Erik B Dam
With the increasing adoption of machine learning tools like neural networks across several domains, interesting connections and comparisons to concepts from other domains are comin…