18 citations · 39 across the 7 of their papers we have counts for
7 papers
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…
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks
Moshe Eliasof, Nir Ben Zikri, Eran Treister
Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. In this paper we propose a novel approach that harn…
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks
Moshe Eliasof, Benjamin Bodner, Eran Treister
Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs). As in…
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…
Mimetic Neural Networks: A unified framework for Protein Design and Folding
Moshe Eliasof, Tue Boesen, Eldad Haber +2
Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design. In this work we introduce a new graph mimet…
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
Moshe Eliasof, Eran Treister
Graph Convolutional Networks (GCNs) have shown to be effective in handling unordered data like point clouds and meshes. In this work we propose novel approaches for graph convoluti…