On the Expressive Power of Permutation-Equivariant Weight-Space Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Dayan, Adir, Eitan, Yam, Maron, Haggai |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FS-KAN: Permutation Equivariant Kolmogorov-Arnold Networks via Function Sharing
by: Elbaz, Ran, et al.
Published: (2025)
by: Elbaz, Ran, et al.
Published: (2025)
On The Expressive Power of GNN Derivatives
by: Eitan, Yam, et al.
Published: (2025)
by: Eitan, Yam, et al.
Published: (2025)
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
by: Bar-Shalom, Guy, et al.
Published: (2024)
by: Bar-Shalom, Guy, et al.
Published: (2024)
Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
by: Southern, Joshua, et al.
Published: (2025)
by: Southern, Joshua, et al.
Published: (2025)
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
by: Eitan, Yam, et al.
Published: (2024)
by: Eitan, Yam, et al.
Published: (2024)
Equivariant Deep Weight Space Alignment
by: Navon, Aviv, et al.
Published: (2023)
by: Navon, Aviv, et al.
Published: (2023)
On the Expressive Power of Spectral Invariant Graph Neural Networks
by: Zhang, Bohang, et al.
Published: (2024)
by: Zhang, Bohang, et al.
Published: (2024)
GradMetaNet: An Equivariant Architecture for Learning on Gradients
by: Gelberg, Yoav, et al.
Published: (2025)
by: Gelberg, Yoav, et al.
Published: (2025)
Training Transformers for KV Cache Compressibility
by: Gelberg, Yoav, et al.
Published: (2026)
by: Gelberg, Yoav, et al.
Published: (2026)
Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models
by: Putterman, Theo, et al.
Published: (2024)
by: Putterman, Theo, et al.
Published: (2024)
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
by: Gai, Jingchu, et al.
Published: (2025)
by: Gai, Jingchu, et al.
Published: (2025)
Improved Generalization of Weight Space Networks via Augmentations
by: Shamsian, Aviv, et al.
Published: (2024)
by: Shamsian, Aviv, et al.
Published: (2024)
On the Reconstruction of Training Data from Group Invariant Networks
by: Elbaz, Ran, et al.
Published: (2024)
by: Elbaz, Ran, et al.
Published: (2024)
It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph
by: Mendelman, Harel, et al.
Published: (2025)
by: Mendelman, Harel, et al.
Published: (2025)
Spanning the Visual Analogy Space with a Weight Basis of LoRAs
by: Manor, Hila, et al.
Published: (2026)
by: Manor, Hila, et al.
Published: (2026)
Learning from Historical Activations in Graph Neural Networks
by: Galron, Yaniv, et al.
Published: (2026)
by: Galron, Yaniv, et al.
Published: (2026)
Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space
by: Bekkers, Erik J, et al.
Published: (2023)
by: Bekkers, Erik J, et al.
Published: (2023)
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
by: Bar-Shalom, Guy, et al.
Published: (2024)
by: Bar-Shalom, Guy, et al.
Published: (2024)
GRANOLA: Adaptive Normalization for Graph Neural Networks
by: Eliasof, Moshe, et al.
Published: (2024)
by: Eliasof, Moshe, et al.
Published: (2024)
A Survey of Weight Space Learning: Understanding, Representation, and Generation
by: Han, Xiaolong, et al.
Published: (2026)
by: Han, Xiaolong, et al.
Published: (2026)
Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks
by: Kinderman, Edan, et al.
Published: (2024)
by: Kinderman, Edan, et al.
Published: (2024)
Neural Message-Passing on Attention Graphs for Hallucination Detection
by: Frasca, Fabrizio, et al.
Published: (2025)
by: Frasca, Fabrizio, et al.
Published: (2025)
Efficient GNN Training Through Structure-Aware Randomized Mini-Batching
by: Balaji, Vignesh, et al.
Published: (2025)
by: Balaji, Vignesh, et al.
Published: (2025)
Efficient Subgraph GNNs by Learning Effective Selection Policies
by: Bevilacqua, Beatrice, et al.
Published: (2023)
by: Bevilacqua, Beatrice, et al.
Published: (2023)
Permutation Equivariant Neural Networks for Symmetric Tensors
by: Pearce-Crump, Edward
Published: (2025)
by: Pearce-Crump, Edward
Published: (2025)
Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
by: Schatzki, Louis, et al.
Published: (2022)
by: Schatzki, Louis, et al.
Published: (2022)
A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
by: Bar-Shalom, Guy, et al.
Published: (2026)
by: Bar-Shalom, Guy, et al.
Published: (2026)
Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViT
by: Bar-Shalom, Guy, et al.
Published: (2025)
by: Bar-Shalom, Guy, et al.
Published: (2025)
Connecting Permutation Equivariant Neural Networks and Partition Diagrams
by: Pearce-Crump, Edward
Published: (2022)
by: Pearce-Crump, Edward
Published: (2022)
Understanding and Improving Laplacian Positional Encodings For Temporal GNNs
by: Galron, Yaniv, et al.
Published: (2025)
by: Galron, Yaniv, et al.
Published: (2025)
Graph Metanetworks for Processing Diverse Neural Architectures
by: Lim, Derek, et al.
Published: (2023)
by: Lim, Derek, et al.
Published: (2023)
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
by: Lim, Derek, et al.
Published: (2024)
by: Lim, Derek, et al.
Published: (2024)
Geometry of Linear Neural Networks: Equivariance and Invariance under Permutation Groups
by: Kohn, Kathlén, et al.
Published: (2023)
by: Kohn, Kathlén, et al.
Published: (2023)
Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power
by: Chen, Yuzhu, et al.
Published: (2025)
by: Chen, Yuzhu, et al.
Published: (2025)
EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks
by: Arbel, Michael, et al.
Published: (2025)
by: Arbel, Michael, et al.
Published: (2025)
On the Expressive Power of Graph Neural Networks
by: Nalwade, Ashwin, et al.
Published: (2024)
by: Nalwade, Ashwin, et al.
Published: (2024)
Permutation-Equivariant 2D State Space Models: Theory and Canonical Architecture for Multivariate Time Series
by: Jeong, Seungwoo, et al.
Published: (2026)
by: Jeong, Seungwoo, et al.
Published: (2026)
Separation Power of Equivariant Neural Networks
by: Pacini, Marco, et al.
Published: (2024)
by: Pacini, Marco, et al.
Published: (2024)
On the Expressive Power of Geometric Graph Neural Networks
by: Joshi, Chaitanya K., et al.
Published: (2023)
by: Joshi, Chaitanya K., et al.
Published: (2023)
Revisiting Multi-Permutation Equivariance through the Lens of Irreducible Representations
by: Sverdlov, Yonatan, et al.
Published: (2024)
by: Sverdlov, Yonatan, et al.
Published: (2024)
Similar Items
-
FS-KAN: Permutation Equivariant Kolmogorov-Arnold Networks via Function Sharing
by: Elbaz, Ran, et al.
Published: (2025) -
On The Expressive Power of GNN Derivatives
by: Eitan, Yam, et al.
Published: (2025) -
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
by: Bar-Shalom, Guy, et al.
Published: (2024) -
Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
by: Southern, Joshua, et al.
Published: (2025) -
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
by: Eitan, Yam, et al.
Published: (2024)