397 citations · 951 across the 46 of their papers we have counts for
51 papers
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
Moshe Eliasof, Eldad Haber, Eran Treister +1
Graph Neural Networks (GNNs) have demonstrated remarkable success in modeling complex relationships in graph-structured data. A recent innovation in this field is the family of Dif…
Feature Transportation Improves Graph Neural Networks
Moshe Eliasof, Eldad Haber, Eran Treister
Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data. However, GNNs still face challenges in modeling complex phenomena…
DRIP: Deep Regularizers for Inverse Problems
Moshe Eliasof, Eldad Haber, Eran Treister
In this paper we consider inverse problems that are mathematically ill-posed. That is, given some (noisy) data, there is more than one solution that approximately fits the data. In…
Semi-Automated Segmentation of Geoscientific Data Using Superpixels
Conrad P. Koziol, Eldad Haber
Geological processes determine the distribution of resources such as critical minerals, water, and geothermal energy. However, direct observation of geology is often prevented by s…
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…
Neural DAEs: Constrained neural networks
Tue Boesen, Eldad Haber, Uri Michael Ascher
This article investigates the effect of explicitly adding auxiliary algebraic trajectory information to neural networks for dynamical systems. We draw inspiration from the field of…