18 citations · 69 across the 60 of their papers we have counts for
4 papers · 1 filter
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…
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
Ido Ben-Yair, Gil Ben Shalom, Moshe Eliasof +1
Quantization of Convolutional Neural Networks (CNNs) is a common approach to ease the computational burden involved in the deployment of CNNs, especially on low-resource edge devic…
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…