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Bibliografiske detaljer
Main Authors:	Tahmasebi, Behrooz, Lim, Derek, Jegelka, Stefanie
Format:	Preprint
Udgivet:	2020
Fag:	Machine Learning
Online adgang:	https://arxiv.org/abs/2012.03174
Tags:	Tilføj Tag Ingen Tags, Vær først til at tagge denne postø!

Lignende værker

Sample Complexity Bounds for Estimating Probability Divergences under Invariances
af: Tahmasebi, Behrooz, et al.
Udgivet: (2023)

The Exact Sample Complexity Gain from Invariances for Kernel Regression
af: Tahmasebi, Behrooz, et al.
Udgivet: (2023)

Learning with Exact Invariances in Polynomial Time
af: Soleymani, Ashkan, et al.
Udgivet: (2025)

Higher-Order Graphon Neural Networks: Approximation and Cut Distance
af: Herbst, Daniel, et al.
Udgivet: (2025)

A Universal Class of Sharpness-Aware Minimization Algorithms
af: Tahmasebi, Behrooz, et al.
Udgivet: (2024)

Geometric Algorithms for Neural Combinatorial Optimization with Constraints
af: Karalias, Nikolaos, et al.
Udgivet: (2025)

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
af: Rauchwerger, Levi, et al.
Udgivet: (2024)

A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
af: Le, Thien, et al.
Udgivet: (2023)

Any-Subgroup Equivariant Networks via Symmetry Breaking
af: Goel, Abhinav, et al.
Udgivet: (2026)

Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry
af: Tahmasebi, Behrooz, et al.
Udgivet: (2025)

Survey on Generalization Theory for Graph Neural Networks
af: Vasileiou, Antonis, et al.
Udgivet: (2025)

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
af: Lim, Derek, et al.
Udgivet: (2024)

Neural Networks With Dense Weights Are Not Universal Approximators
af: Rauchwerger, Levi, et al.
Udgivet: (2026)

A Canonicalization Perspective on Invariant and Equivariant Learning
af: Ma, George, et al.
Udgivet: (2024)

Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models
af: Putterman, Theo, et al.
Udgivet: (2024)

Learning to Approximate Uniform Facility Location via Graph Neural Networks
af: Qian, Chendi, et al.
Udgivet: (2026)

Are Graph Neural Networks Optimal Approximation Algorithms?
af: Yau, Morris, et al.
Udgivet: (2023)

Learning Efficient Positional Encodings with Graph Neural Networks
af: Kanatsoulis, Charilaos I., et al.
Udgivet: (2025)

Demystifying Higher-Order Graph Neural Networks
af: Besta, Maciej, et al.
Udgivet: (2024)

Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
af: Moradi, Parsa, et al.
Udgivet: (2024)

Subsampling Graphs with GNN Performance Guarantees
af: Jain, Mika Sarkin, et al.
Udgivet: (2025)

An Efficient Subgraph GNN with Provable Substructure Counting Power
af: Yan, Zuoyu, et al.
Udgivet: (2023)

Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions
af: Giusti, Lorenzo
Udgivet: (2024)

NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation
af: Zhuang, Yuanxin, et al.
Udgivet: (2025)

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
af: Guo, Xiaojun, et al.
Udgivet: (2025)

Spectral Higher-Order Neural Networks
af: Peri, Gianluca, et al.
Udgivet: (2026)

Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
af: Giraldo, Jhony H., et al.
Udgivet: (2024)

Learning to Count Isomorphisms with Graph Neural Networks
af: Yu, Xingtong, et al.
Udgivet: (2023)

Future Directions in the Theory of Graph Machine Learning
af: Morris, Christopher, et al.
Udgivet: (2024)

GoMS: Graph of Molecule Substructure Network for Molecule Property Prediction
af: Qu, Shuhui, et al.
Udgivet: (2025)

On the Emergence of Position Bias in Transformers
af: Wu, Xinyi, et al.
Udgivet: (2025)

CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
af: Buffelli, Davide, et al.
Udgivet: (2024)

Beyond Arrow: From Impossibility to Possibilities in Multi-Criteria Benchmarking
af: Gordienko, Polina, et al.
Udgivet: (2026)

Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks
af: Lee, Su Hyeong, et al.
Udgivet: (2025)

Higher-Order Topological Directionality and Directed Simplicial Neural Networks
af: Lecha, Manuel, et al.
Udgivet: (2024)

Disentangled Graph Representation Based on Substructure-Aware Graph Optimal Matching Kernel Convolutional Networks
af: Wang, Mao, et al.
Udgivet: (2025)

On the Stability of Expressive Positional Encodings for Graphs
af: Huang, Yinan, et al.
Udgivet: (2023)

A projection-based framework for gradient-free and parallel learning
af: Bergmeister, Andreas, et al.
Udgivet: (2025)

LieAugmenter: Equivariant Learning by Discovering Symmetries with Learnable Augmentations
af: Santos-Escriche, Eduardo, et al.
Udgivet: (2025)

Homomorphism Counts for Graph Neural Networks: All About That Basis
af: Jin, Emily, et al.
Udgivet: (2024)