Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods. - Moshe Eliasof; Andrei Sharf; Eran Treister (CoRR, 2019)

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling. - Moshe Eliasof; Eran Treister (NeurIPS, 2020)

Multigrid-in-Channels Neural Network Architectures. - Moshe Eliasof; Jonathan Ephrath; Lars Ruthotto; Eran Treister (CoRR, 2020)

LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks. - Jonathan Ephrath; Moshe Eliasof; Lars Ruthotto; Eldad Haber; Eran Treister (IEEE J. Sel. Top. Signal Process., 2020)

Multimodal 3D Shape Reconstruction under Calibration Uncertainty Using Parametric Level Set Methods. - Moshe Eliasof; Andrei Sharf; Eran Treister (SIAM J. Imaging Sci., 2020)

PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations. - Moshe Eliasof; Eldad Haber; Eran Treister (NeurIPS, 2021)

Quantized convolutional neural networks through the lens of partial differential equations. - Ido Ben-Yair; Gil Ben Shalom; Moshe Eliasof; Eran Treister (CoRR, 2021)

Rethinking Unsupervised Neural Superpixel Segmentation. - Moshe Eliasof; Nir Ben Zikri; Eran Treister (ICIP, 2022)

pathGCN: Learning General Graph Spatial Operators from Paths. - Moshe Eliasof; Eldad Haber; Eran Treister (ICML, 2022)

Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks. - Moshe Eliasof; Nir Ben Zikri; Eran Treister (CoRR, 2022)

ωGNNs: Deep Graph Neural Networks Enhanced by Multiple Propagation Operators. - Moshe Eliasof; Lars Ruthotto; Eran Treister (CoRR, 2022)

Mimetic Neural Networks: A Unified Framework for Protein Design and Folding. - Moshe Eliasof; Tue Boesen; Eldad Haber; Chen Keasar; Eran Treister (Frontiers Bioinform., 2022)

Graph Positional Encoding via Random Feature Propagation. - Moshe Eliasof; Fabrizio Frasca; Beatrice Bevilacqua; Eran Treister; Gal Chechik; Haggai Maron (ICML, 2023)

Improving Graph Neural Networks with Learnable Propagation Operators. - Moshe Eliasof; Lars Ruthotto; Eran Treister (ICML, 2023)

DRIP: Deep Regularizers for Inverse Problems. - Moshe Eliasof; Eldad Haber; Eran Treister (CoRR, 2023)

ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks. - Moshe Eliasof; Eldad Haber; Eran Treister (CoRR, 2023)

Contractive Systems Improve Graph Neural Networks Against Adversarial Attacks. - Moshe Eliasof; Davide Murari; Ferdia Sherry; Carola-Bibiane Schönlieb (CoRR, 2023)

MGIC: Multigrid-in-Channels Neural Network Architectures. - Moshe Eliasof; Jonathan Ephrath; Lars Ruthotto; Eran Treister (SIAM J. Sci. Comput., 2023)

Estimating a Potential Without the Agony of the Partition Function. - Eldad Haber; Moshe Eliasof; Luis Tenorio (SIAM J. Math. Data Sci., 2023)

Feature Transportation Improves Graph Neural Networks. - Moshe Eliasof; Eldad Haber; Eran Treister (AAAI, 2024)

On The Temporal Domain of Differential Equation Inspired Graph Neural Networks. - Moshe Eliasof; Eldad Haber; Eran Treister; Carola-Bibiane Schönlieb (AISTATS, 2024)

Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification. - Moshe Eliasof; Eldad Haber; Eran Treister (ECAI, 2024)

Resilient Graph Neural Networks: A Coupled Dynamical Systems Approach. - Moshe Eliasof; Davide Murari; Ferdia Sherry; Carola-Bibiane Schönlieb (ECAI, 2024)

Efficient Subgraph GNNs by Learning Effective Selection Policies. - Beatrice Bevilacqua; Moshe Eliasof; Eli A. Meirom; Bruno Ribeiro 001; Haggai Maron (ICLR, 2024)

GRANOLA: Adaptive Normalization for Graph Neural Networks. - Moshe Eliasof; Beatrice Bevilacqua; Carola-Bibiane Schönlieb; Haggai Maron (NeurIPS, 2024)

DiGRAF: Diffeomorphic Graph-Adaptive Activation Function. - Krishna Sri Ipsit Mantri; Xinzhi Wang; Carola-Bibiane Schönlieb; Bruno Ribeiro 001; Beatrice Bevilacqua; Moshe Eliasof (NeurIPS, 2024)

Advection Augmented Convolutional Neural Networks. - Niloufar Zakariaei; Siddharth Rout; Eldad Haber; Moshe Eliasof (NeurIPS, 2024)

An Over Complete Deep Learning Method for Inverse Problems. - Moshe Eliasof; Eldad Haber; Eran Treister (CoRR, 2024)

Graph Neural Networks for Binary Programming. - Moshe Eliasof; Eldad Haber (CoRR, 2024)

Tackling Graph Oversquashing by Global and Local Non-Dissipativity. - Alessio Gravina; Moshe Eliasof; Claudio Gallicchio; Davide Bacciu; Carola-Bibiane Schönlieb (CoRR, 2024)

Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling. - Stathis Megas; Daniel G. Chen; Krzysztof Polanski; Moshe Eliasof; Carola-Bibiane Schönlieb; Sarah A. Teichmann (CoRR, 2024)

A General Recipe for Contractive Graph Neural Networks - Technical Report. - Maya Bechler-Speicher; Moshe Eliasof (CoRR, 2024)

On the Utilization of Unique Node Identifiers in Graph Neural Networks. - Maya Bechler-Speicher; Moshe Eliasof; Carola-Bibiane Schönlieb; Ran Gilad-Bachrach; Amir Globerson (CoRR, 2024)

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs. - Fabrizio Frasca; Fabian Jogl; Moshe Eliasof; Matan Ostrovsky; Carola-Bibiane Schönlieb; Thomas Gärtner 001; Haggai Maron (CoRR, 2024)

Global-local graph neural networks for node-classification. - Moshe Eliasof; Eran Treister (Pattern Recognit. Lett., 2024)

Graph Neural Reaction Diffusion Models. - Moshe Eliasof; Eldad Haber; Eran Treister (SIAM J. Sci. Comput., 2024)

Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks. - Moshe Eliasof; Benjamin Jacob Bodner; Eran Treister (IEEE Trans. Neural Networks Learn. Syst., 2024)

Learning Regularization for Graph Inverse Problems. - Moshe Eliasof; Md Shahriar Rahim Siddiqui; Carola-Bibiane Schönlieb; Eldad Haber (AAAI, 2025)

On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems. - Alessio Gravina; Moshe Eliasof; Claudio Gallicchio; Davide Bacciu; Carola-Bibiane Schönlieb (AAAI, 2025)

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations. - Krishna Sri Ipsit Mantri; Carola-Bibiane Schönlieb; Bruno Ribeiro 001; Chaim Baskin; Moshe Eliasof (CVPR, 2025)

Estimation of single-cell and tissue perturbation effect in spatial transcriptomics via Spatial Causal Disentanglement. - Stathis Megas; Daniel G. Chen; Krzysztof Polanski; Moshe Eliasof; Carola-Bibiane Schönlieb; Sarah A. Teichmann (ICLR, 2025)

Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings. - Billy Joe Franks; Moshe Eliasof; Semih Cantürk; Guy Wolf; Carola-Bibiane Schönlieb; Sophie Fellenz; Marius Kloft (Trans. Mach. Learn. Res., 2025)

GRAMA: Adaptive Graph Autoregressive Moving Average Models. - Moshe Eliasof; Alessio Gravina; Andrea Ceni; Claudio Gallicchio; Davide Bacciu; Carola-Bibiane Schönlieb (CoRR, 2025)

Multiscale Training of Convolutional Neural Networks. - Niloufar Zakariaei; Shadab Ahamed; Eldad Haber; Moshe Eliasof (CoRR, 2025)

On the Effectiveness of Random Weights in Graph Neural Networks. - Thu Bui; Carola-Bibiane Schönlieb; Bruno Ribeiro 001; Beatrice Bevilacqua; Moshe Eliasof (CoRR, 2025)

Towards Invariance to Node Identifiers in Graph Neural Networks. - Maya Bechler-Speicher; Moshe Eliasof; Carola-Bibiane Schönlieb; Ran Gilad-Bachrach; Amir Globerson (CoRR, 2025)

Iterative Flow Matching - Path Correction and Gradual Refinement for Enhanced Generative Modeling. - Eldad Haber; Shadab Ahamed; Md Shahriar Rahim Siddiqui; Niloufar Zakariaei; Moshe Eliasof (CoRR, 2025)

Towards Efficient Training of Graph Neural Networks: A Multiscale Approach. - Eshed Gal; Moshe Eliasof; Carola-Bibiane Schönlieb; Eldad Haber; Eran Treister (CoRR, 2025)

FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks. - Or Feldman; Krishna Sri Ipsit Mantri; Carola-Bibiane Schönlieb; Chaim Baskin; Moshe Eliasof (CoRR, 2025)

One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling. - Nimrod Berman; Ilan Naiman; Moshe Eliasof; Hedi Zisling; Omri Azencot (CoRR, 2025)

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling. - Andrea Ceni; Alessio Gravina; Claudio Gallicchio; Davide Bacciu; Carola-Bibiane Schönlieb; Moshe Eliasof (CoRR, 2025)

Improving the Effective Receptive Field of Message-Passing Neural Networks. - Shahaf E. Finder; Ron Shapira Weber; Moshe Eliasof; Oren Freifeld; Eran Treister (CoRR, 2025)

Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields. - Md Shahriar Rahim Siddiqui; Moshe Eliasof; Eldad Haber (CoRR, 2025)

Understanding and Improving Laplacian Positional Encodings For Temporal GNNs. - Yaniv Galron; Fabrizio Frasca; Haggai Maron; Eran Treister; Moshe Eliasof (CoRR, 2025)

Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks. - Ali Hariri; Alvaro Arroyo; Alessio Gravina; Moshe Eliasof; Carola-Bibiane Schönlieb; Davide Bacciu; Kamyar Azizzadenesheli; Xiaowen Dong 001; Pierre Vandergheynst (CoRR, 2025)

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows. - Moshe Eliasof; Eldad Haber; Carola-Bibiane Schönlieb (CoRR, 2025)