24 citations · 63 across the 9 of their papers we have counts for
9 papers
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data
Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentat…
Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
Keegan Lensink, Issam Laradji, Marco Law +4
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread into a global pandemic. A form of pneumonia, presenting as opacities with in a patient's lungs,…
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
Issam Laradji, Pau Rodriguez, Frederic Branchaud-Charron +5
One of the key challenges in the battle against the Coronavirus (COVID-19) pandemic is to detect and quantify the severity of the disease in a timely manner. Computed tomographies…
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Issam Laradji, Pau Rodriguez, Oscar Mañas +6
Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world causing an existential health crisis. Thus, having a system that automatically detects COVID-19 in tomo…
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing
Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface cha…
Symmetric block-low-rank layers for fully reversible multilevel neural networks
Bas Peters, Eldad Haber, Keegan Lensink
Factors that limit the size of the input and output of a neural network include memory requirements for the network states/activations to compute gradients, as well as memory for t…
Fluid Flow Mass Transport for Generative Networks
Jingrong Lin, Keegan Lensink, Eldad Haber
Generative Adversarial Networks have been shown to be powerful in generating content. To this end, they have been studied intensively in the last few years. Nonetheless, training t…
Fully Hyperbolic Convolutional Neural Networks
Keegan Lensink, Bas Peters, Eldad Haber
Convolutional Neural Networks (CNN) have recently seen tremendous success in various computer vision tasks. However, their application to problems with high dimensional input and o…
IMEXnet: A Forward Stable Deep Neural Network
Eldad Haber, Keegan Lensink, Eran Treister +1
Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use. These challeng…