78 citations · 215 across the 52 of their papers we have counts for
58 papers
Dispersion-corrected compact finite difference discretizations for the elastic Helmholtz equation in two and three dimensions
Rachel Yovel, Eli Turkel, Eran Treister
We develop a family of compact, dispersion-corrected marker-and-cell (MAC)-based finite-difference discretizations for the elastic Helmholtz equation. While dispersion correction i…
Fast and Memory-Efficient Wavelet Convolutions via I/O-Aware Reformulation
Amit Aflalo, Shahaf E. Finder, Roy Amoyal +2
Wavelet convolution (WTConv) has emerged as an increasingly popular drop-in replacement for standard convolutions, expanding a network's receptive field exponentially with the numb…
RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections
Yali Fink, Ido Ben-Yair, Lars Ruthotto +1
The scalable solution of large sparse linear systems is a bottleneck in scientific computing and graph analysis. While algebraic multigrid (AMG) offers optimal linear scaling, its…
Scalable Multigrid Solver for the Helmholtz Equation: Real-Shifted Coarse Grid Correction
Rachel Yovel, Eran Treister
We present a convergent and scalable multigrid solver for high-frequency Helmholtz equations. Standard multigrid methods do not converge for high-frequency Helmholtz problems, and…
Reversing Large Language Models for Efficient Training and Fine-Tuning
Eshed Gal, Moshe Eliasof, Javier Turek +3
Large Language Models (LLMs) are known for their expensive and time-consuming training. Thus, oftentimes, LLMs are fine-tuned to address a specific task, given the pretrained weigh…
Vanka-smoothed shifted Laplacian multigrid preconditioners for the Helmholtz equations
Rachel Yovel, Yunhui He, Eran Treister
We present an improved multigrid preconditioner for the acoustic Helmholtz equation with enhanced scalability. Standard multigrid fails to converge for the Helmholtz equation, and…
Zero-Shot Detection of LLM-Generated Code via Approximated Task Conditioning
Maor Ashkenazi, Ofir Brenner, Tal Furman Shohet +1
Detecting Large Language Model (LLM)-generated code is a growing challenge with implications for security, intellectual property, and academic integrity. We investigate the role of…
Understanding and Improving Laplacian Positional Encodings For Temporal GNNs
Yaniv Galron, Fabrizio Frasca, Haggai Maron +2
Temporal graph learning has applications in recommendation systems, traffic forecasting, and social network analysis. Although multiple architectures have been introduced, progress…
Improving the Effective Receptive Field of Message-Passing Neural Networks
Shahaf E. Finder, Ron Shapira Weber, Moshe Eliasof +2
Message-Passing Neural Networks (MPNNs) have become a cornerstone for processing and analyzing graph-structured data. However, their effectiveness is often hindered by phenomena su…
Towards Efficient Training of Graph Neural Networks: A Multiscale Approach
Eshed Gal, Moshe Eliasof, Carola-Bibiane Schönlieb +3
Graph Neural Networks (GNNs) have become powerful tools for learning from graph-structured data, finding applications across diverse domains. However, as graph sizes and connectivi…
A SIMPLE-Based Preconditioned Solver for the Direct-Forcing Immersed Boundary Method
Rachel Yovel, Eran Treister, Yuri Feldman
We present a robust and scalable solver for direct-forcing immersed boundary simulations, based on a preconditioned SIMPLE algorithm. The method applies block elimination to the pr…
A block-acoustic preconditioner for the elastic Helmholtz equation
Rachel Yovel, Eran Treister
We present a novel block-preconditioner for the elastic Helmholtz equation, based on a reduction to acoustic Helmholtz equations. Both versions of the Helmholtz equations are chall…
Towards Croppable Implicit Neural Representations
Maor Ashkenazi, Eran Treister
Implicit Neural Representations (INRs) have peaked interest in recent years due to their ability to encode natural signals using neural networks. While INRs allow for useful applic…
Wavelet Convolutions for Large Receptive Fields
Shahaf E. Finder, Roy Amoyal, Eran Treister +1
In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers' (ViTs) self-at…
Graph Neural Reaction Diffusion Models
Moshe Eliasof, Eldad Haber, Eran Treister
The integration of Graph Neural Networks (GNNs) and Neural Ordinary and Partial Differential Equations has been extensively studied in recent years. GNN architectures powered by ne…
Global-Local Graph Neural Networks for Node-Classification
Moshe Eliasof, Eran Treister
The task of graph node classification is often approached by utilizing a local Graph Neural Network (GNN), that learns only local information from the node input features and their…
Physics-guided Full Waveform Inversion using Encoder-Solver Convolutional Neural Networks
Matan Goren, Eran Treister
Full Waveform Inversion (FWI) is an inverse problem for estimating the wave velocity distribution in a given domain, based on observed data on the boundaries. The inversion is comp…
An Over Complete Deep Learning Method for Inverse Problems
Moshe Eliasof, Eldad Haber, Eran Treister
Obtaining meaningful solutions for inverse problems has been a major challenge with many applications in science and engineering. Recent machine learning techniques based on proxim…
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…
The determinant of the Laplacian matrix of a quaternion unit gain graph
Ivan I. Kyrchei, Eran Treister, Volodymyr O. Pelykh
A quaternion unit gain graph is a graph where each orientation of an edge is given a quaternion unit, and the opposite orientation is assigned the inverse of this quaternion unit.…
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…
LFA-tuned matrix-free multigrid method for the elastic Helmholtz equation
Rachel Yovel, Eran Treister
We present an efficient matrix-free geometric multigrid method for the elastic Helmholtz equation, and a suitable discretization. Many discretization methods had been considered in…
Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layers
Bar Lerer, Ido Ben-Yair, Eran Treister
We present a deep learning-based iterative approach to solve the discrete heterogeneous Helmholtz equation for high wavenumbers. Combining classical iterative multigrid solvers and…
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…