Moshe Eliasof

According to our database1, Moshe Eliasof authored at least 25 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
An Over Complete Deep Learning Method for Inverse Problems.
CoRR, 2024

On The Temporal Domain of Differential Equation Inspired Graph Neural Networks.
CoRR, 2024

Feature Transportation Improves Graph Neural Networks.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Estimating a Potential Without the Agony of the Partition Function.
SIAM J. Math. Data Sci., December, 2023

MGIC: Multigrid-in-Channels Neural Network Architectures.
SIAM J. Sci. Comput., June, 2023

Contractive Systems Improve Graph Neural Networks Against Adversarial Attacks.
CoRR, 2023

Efficient Subgraph GNNs by Learning Effective Selection Policies.
CoRR, 2023

ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks.
CoRR, 2023

DRIP: Deep Regularizers for Inverse Problems.
CoRR, 2023

Improving Graph Neural Networks with Learnable Propagation Operators.
Proceedings of the International Conference on Machine Learning, 2023

Graph Positional Encoding via Random Feature Propagation.
Proceedings of the International Conference on Machine Learning, 2023

2022
Mimetic Neural Networks: A Unified Framework for Protein Design and Folding.
Frontiers Bioinform., 2022

Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification.
CoRR, 2022

ωGNNs: Deep Graph Neural Networks Enhanced by Multiple Propagation Operators.
CoRR, 2022

Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks.
CoRR, 2022

pathGCN: Learning General Graph Spatial Operators from Paths.
Proceedings of the International Conference on Machine Learning, 2022

Rethinking Unsupervised Neural Superpixel Segmentation.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

2021
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks.
CoRR, 2021

Quantized convolutional neural networks through the lens of partial differential equations.
CoRR, 2021

PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Multimodal 3D Shape Reconstruction under Calibration Uncertainty Using Parametric Level Set Methods.
SIAM J. Imaging Sci., 2020

LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks.
IEEE J. Sel. Top. Signal Process., 2020

Multigrid-in-Channels Neural Network Architectures.
CoRR, 2020

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods.
CoRR, 2019


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