:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Porras-Valenzuela, Javier, Wang, Zhiyang, Shang, Xiaotao, Wang, Yusu, Ribeiro, Alejandro
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.15239
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Comparing Graph Transformers via Positional Encodings
by: Black, Mitchell, et al.
Published: (2024)

A Constrained Optimization Perspective of Unrolled Transformers
by: Porras-Valenzuela, Javier, et al.
Published: (2026)

Loss Shaping Constraints for Long-Term Time Series Forecasting
by: Hounie, Ignacio, et al.
Published: (2024)

A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
by: Wang, Zhiyang, et al.
Published: (2024)

Generalization of Graph Neural Networks is Robust to Model Mismatch
by: Wang, Zhiyang, et al.
Published: (2024)

Generalization of Geometric Graph Neural Networks with Lipschitz Loss Functions
by: Wang, Zhiyang, et al.
Published: (2024)

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

On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
by: Zhou, Cai, et al.
Published: (2024)

Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding
by: Wang, Zhe, et al.
Published: (2024)

Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back
by: Battiloro, Claudio, et al.
Published: (2023)

Stability to Deformations of Manifold Filters and Manifold Neural Networks
by: Wang, Zhiyang, et al.
Published: (2021)

Graph Transformers without Positional Encodings
by: Garg, Ayush
Published: (2024)

Benchmarking Positional Encodings for GNNs and Graph Transformers
by: Grötschla, Florian, et al.
Published: (2024)

What Are Good Positional Encodings for Directed Graphs?
by: Huang, Yinan, et al.
Published: (2024)

Tab-PET: Graph-Based Positional Encodings for Tabular Transformers
by: Leng, Yunze, et al.
Published: (2025)

Graph Transformer with Disease Subgraph Positional Encoding for Improved Comorbidity Prediction
by: Qin, Xihan, et al.
Published: (2025)

What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
by: Li, Hongkang, et al.
Published: (2024)

On the Geometry of Positional Encodings in Transformers
by: Cirrincione, Giansalvo
Published: (2026)

Rotary Position Encodings for Graphs
by: Reid, Isaac, et al.
Published: (2025)

Rethinking the Role of Positional Encoding: Sliding-Window Transformers without PE Remain Turing Complete
by: Li, Qian, et al.
Published: (2026)

Learning Ultrametric Trees for Optimal Transport Regression
by: Chen, Samantha, et al.
Published: (2022)

Distances for Markov Chains, and Their Differentiation
by: Brugère, Tristan, et al.
Published: (2023)

Multi-Target Tracking with Transferable Convolutional Neural Networks
by: Owerko, Damian, et al.
Published: (2022)

General and Efficient Steering of Unconditional Diffusion
by: Wang, Qingsong, et al.
Published: (2026)

Improving Transformers using Faithful Positional Encoding
by: Idé, Tsuyoshi, et al.
Published: (2024)

Graph neural networks extrapolate out-of-distribution for shortest paths
by: Nerem, Robert R., et al.
Published: (2025)

Learning Laplacian Positional Encodings for Heterophilous Graphs
by: Ito, Michael, et al.
Published: (2025)

T-GAE: Transferable Graph Autoencoder for Network Alignment
by: He, Jiashu, et al.
Published: (2023)

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

RKHS Representation of Algebraic Convolutional Filters with Integral Operators
by: Parada-Mayorga, Alejandro, et al.
Published: (2026)

DAM-GT: Dual Positional Encoding-Based Attention Masking Graph Transformer for Node Classification
by: Li, Chenyang, et al.
Published: (2025)

Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries and Denoisers
by: Wan, Zhengchao, et al.
Published: (2024)

Vocabulary In-Context Learning in Transformers: Benefits of Positional Encoding
by: Ma, Qian, et al.
Published: (2025)

Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers
by: Choromanski, Krzysztof Marcin, et al.
Published: (2023)

Transformer with Koopman-Enhanced Graph Convolutional Network for Spatiotemporal Dynamics Forecasting
by: Wang, Zekai, et al.
Published: (2025)

Convolutional Filtering with RKHS Algebras
by: Parada-Mayorga, Alejandro, et al.
Published: (2024)

Supra-Laplacian Encoding for Transformer on Dynamic Graphs
by: Karmim, Yannis, et al.
Published: (2024)

Explaining GNN Explanations with Edge Gradients
by: He, Jesse, et al.
Published: (2025)

On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)

Revisiting Convergence of AdaGrad with Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)