24 citations · 63 across the 9 of their papers we have counts for
4 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…
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…