86 citations · 233 across the 15 of their papers we have counts for
8 papers · 1 filter
Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification
Moshe Eliasof, Eldad Haber, Eran Treister
Graph Neural Networks (GNNs) are prominent in handling sparse and unstructured data efficiently and effectively. Specifically, GNNs were shown to be highly effective for node class…
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
Moshe Eliasof, Eldad Haber, Eran Treister
Graph neural networks are increasingly becoming the go-to approach in various fields such as computer vision, computational biology and chemistry, where data are naturally explaine…
An Introduction to Deep Generative Modeling
Lars Ruthotto, Eldad Haber
Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions using a large number of samp…
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…
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks
Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto +2
Convolutional Neural Networks (CNNs) have become indispensable for solving machine learning tasks in speech recognition, computer vision, and other areas that involve high-dimensio…
LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks
Jonathan Ephrath, Lars Ruthotto, Eldad Haber +1
Convolutional Neural Networks (CNNs) filter the input data using spatial convolution operators with compact stencils. Commonly, the convolution operators couple features from all c…