86 citations · 233 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
Deep Neural Network Accelerated Implicit Filtering
Brian Irwin, Eldad Haber, Raviv Gal +1
In this paper, we illustrate a novel method for solving optimization problems when derivatives are not explicitly available. We show that combining implicit filtering (IF), an exis…
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…
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…