Search Results for author: Moshe Eliasof

Found 17 papers, 4 papers with code

ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks

no code implementations29 Jul 2023 Moshe Eliasof, Eldad Haber, Eran Treister

Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data.

Node Classification

DRIP: Deep Regularizers for Inverse Problems

no code implementations30 Mar 2023 Moshe Eliasof, Eldad Haber, Eran Treister

First, most techniques cannot guarantee that the solution fits the data at inference.

Deblurring Image Deblurring

Graph Positional Encoding via Random Feature Propagation

no code implementations6 Mar 2023 Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron

Two main families of node feature augmentation schemes have been explored for enhancing GNNs: random features and spectral positional encoding.

Graph Classification Node Classification

Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification

no code implementations29 Nov 2022 Moshe Eliasof, Eldad Haber, Eran Treister

In this paper, we propose novel objective terms for the training of GNNs for node classification, aiming to exploit all the available data and improve accuracy.

Node Classification

Estimating a potential without the agony of the partition function

no code implementations19 Aug 2022 Eldad Haber, Moshe Eliasof, Luis Tenorio

In this paper we propose an alternative approach based on Maximum A-Posteriori (MAP) estimators, we name Maximum Recovery MAP (MR-MAP), to derive estimators that do not require the computation of the partition function, and reformulate the problem as an optimization problem.

pathGCN: Learning General Graph Spatial Operators from Paths

no code implementations15 Jul 2022 Moshe Eliasof, Eldad Haber, Eran Treister

In the context of GCNs, differently from CNNs, a pre-determined spatial operator based on the graph Laplacian is often chosen, allowing only the point-wise operations to be learnt.

Rethinking Unsupervised Neural Superpixel Segmentation

no code implementations21 Jun 2022 Moshe Eliasof, Nir Ben Zikri, Eran Treister

Recently, the concept of unsupervised learning for superpixel segmentation via CNNs has been studied.


Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks

no code implementations10 Oct 2021 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).

Feature Compression Node Classification +3

Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations

no code implementations NeurIPS Workshop DLDE 2021 Ido Ben-Yair, Gil Ben Shalom, Moshe Eliasof, Eran Treister

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 devices.

Autonomous Driving Image Classification +2

PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations

1 code implementation NeurIPS 2021 Moshe Eliasof, Eldad Haber, Eran Treister

Moreover, as we demonstrate using an extensive set of experiments, our PDE-motivated networks can generalize and be effective for various types of problems from different fields.

Mimetic Neural Networks: A unified framework for Protein Design and Folding

no code implementations7 Feb 2021 Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister

Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design.

BIG-bench Machine Learning Protein Design +1

MGIC: Multigrid-in-Channels Neural Network Architectures

1 code implementation NeurIPS Workshop DLDE 2021 Moshe Eliasof, Jonathan Ephrath, Lars Ruthotto, Eran Treister

We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs).

Image Classification Point Cloud Classification

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling

1 code implementation NeurIPS 2020 Moshe Eliasof, Eran Treister

Graph Convolutional Networks (GCNs) have shown to be effective in handling unordered data like point clouds and meshes.

LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks

no code implementations29 Oct 2019 Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto, Eldad Haber, Eran Treister

In practice, the input data and the hidden features consist of a large number of channels, which in most CNNs are fully coupled by the convolution operators.

Image Classification Semantic Segmentation +2

Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods

2 code implementations23 Apr 2019 Moshe Eliasof, Andrei Sharf, Eran Treister

This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters.

3D Shape Reconstruction

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