86 citations · 233 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…
Automatic classification of geologic units in seismic images using partially interpreted examples
Bas Peters, Justin Granek, Eldad Haber
Geologic interpretation of large seismic stacked or migrated seismic images can be a time-consuming task for seismic interpreters. Neural network based semantic segmentation provid…
GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport
Rena Elkin, Saad Nadeem, Eldad Haber +4
The glymphatic system (GS) is a transit passage that facilitates brain metabolic waste removal and its dysfunction has been associated with neurodegenerative diseases such as Alzhe…
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Bo Chang, Lili Meng, Eldad Haber +3
Recently, deep residual networks have been successfully applied in many computer vision and natural language processing tasks, pushing the state-of-the-art performance with deeper…