24 citations · 61 across the 12 of their papers we have counts for
12 papers
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…
Diffraction Tomography with Helmholtz Equation: Efficient and Robust Multigrid-Based Solver
Tao Hong, Thanh-an Pham, Eran Treister +1
Diffraction tomography is a noninvasive technique that estimates the refractive indices of unknown objects and involves an inverse-scattering problem governed by the wave equation.…
GradFreeBits: Gradient Free Bit Allocation for Dynamic Low Precision Neural Networks
Benjamin J. Bodner, Gil Ben Shalom, Eran Treister
Quantized neural networks (QNNs) are among the main approaches for deploying deep neural networks on low resource edge devices. Training QNNs using different levels of precision th…
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…
Full waveform inversion using extended and simultaneous sources
Sagi Buchatsky, Eran Treister
PDE-constrained optimization problems are often treated using the reduced formulation where the PDE constraints are eliminated. This approach is known to be more computationally fe…