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Publication search results
found 84 matches
- 2024
- Christopher Morris, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka:
Future Directions in Foundations of Graph Machine Learning. CoRR abs/2402.02287 (2024) - Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron:
Improved Generalization of Weight Space Networks via Augmentations. CoRR abs/2402.04081 (2024) - Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron:
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products. CoRR abs/2402.08450 (2024) - Yoni Kasten, Wuyue Lu, Haggai Maron:
Learning Priors for Non Rigid SfM from Casual Videos. CoRR abs/2404.07097 (2024) - Moshe Eliasof, Beatrice Bevilacqua, Carola-Bibiane Schönlieb, Haggai Maron:
GRANOLA: Adaptive Normalization for Graph Neural Networks. CoRR abs/2404.13344 (2024) - Derek Lim, Moe Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka:
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof. CoRR abs/2405.20231 (2024) - Bohang Zhang, Lingxiao Zhao, Haggai Maron:
On the Expressive Power of Spectral Invariant Graph Neural Networks. CoRR abs/2406.04336 (2024) - Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron:
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening. CoRR abs/2406.09291 (2024) - 2023
- Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal, Onur Yilmaz, Haggai Maron, Matthew West:
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching. DaWaK 2023: 303-315 - Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka:
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. ICLR 2023 - Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron:
Graph Positional Encoding via Random Feature Propagation. ICML 2023: 9202-9223 - Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron:
Equivariant Architectures for Learning in Deep Weight Spaces. ICML 2023: 25790-25816 - Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, Yaron Lipman:
Equivariant Polynomials for Graph Neural Networks. ICML 2023: 28191-28222 - Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron:
Expressive Sign Equivariant Networks for Spectral Geometric Learning. NeurIPS 2023 - Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, Gal Chechik:
Norm-guided latent space exploration for text-to-image generation. NeurIPS 2023 - Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron:
Equivariant Architectures for Learning in Deep Weight Spaces. CoRR abs/2301.12780 (2023) - Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, Yaron Lipman:
Equivariant Polynomials for Graph Neural Networks. CoRR abs/2302.11556 (2023) - Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron:
Graph Positional Encoding via Random Feature Propagation. CoRR abs/2303.02918 (2023) - Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal, Onur Yilmaz, Haggai Maron, Matthew West:
Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching. CoRR abs/2304.03215 (2023) - Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, Gal Chechik:
Norm-guided latent space exploration for text-to-image generation. CoRR abs/2306.08687 (2023) - Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron:
Equivariant Deep Weight Space Alignment. CoRR abs/2310.13397 (2023) - Beatrice Bevilacqua, Moshe Eliasof, Eli A. Meirom, Bruno Ribeiro, Haggai Maron:
Efficient Subgraph GNNs by Learning Effective Selection Policies. CoRR abs/2310.20082 (2023) - Aviv Shamsian, David W. Zhang, Aviv Navon, Yan Zhang, Miltiadis Kofinas, Idan Achituve, Riccardo Valperga, Gertjan J. Burghouts, Efstratios Gavves, Cees G. M. Snoek, Ethan Fetaya, Gal Chechik, Haggai Maron:
Data Augmentations in Deep Weight Spaces. CoRR abs/2311.08851 (2023) - Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron:
Expressive Sign Equivariant Networks for Spectral Geometric Learning. CoRR abs/2312.02339 (2023) - Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas:
Graph Metanetworks for Processing Diverse Neural Architectures. CoRR abs/2312.04501 (2023) - 2022
- Rinon Gal
, Or Patashnik, Haggai Maron, Amit H. Bermano
, Gal Chechik, Daniel Cohen-Or:
StyleGAN-NADA: CLIP-guided domain adaptation of image generators. ACM Trans. Graph. 41(4): 141:1-141:13 (2022) - Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron:
Equivariant Subgraph Aggregation Networks. ICLR 2022 - Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik:
Optimizing Tensor Network Contraction Using Reinforcement Learning. ICML 2022: 15278-15292 - Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Multi-Task Learning as a Bargaining Game. ICML 2022: 16428-16446 - Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, Haggai Maron:
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. NeurIPS 2022
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