Yi Guo

Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion

2023 · arXiv (Cornell University)

This paper presents a comprehensive theoretical analysis of the graph p-Laplacian regularized framelet network (pL-UFG) to establish a solid understanding of its properties. We conduct a convergence analysis on pL-UFG, addressing the gap in the …

Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond

2023 · arXiv (Cornell University)

Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data. Despite their great success, critical computational challenges such as over-smoothing, over-squashing, and limited expressive power continue to …

[MASK] Insertion: a robust method for anti-adversarial attacks

2023

Adversarial attack aims to perturb input sequences and mislead a trained model for false predictions. To enhance the model robustness, defensing methods are accordingly employed by either data augmentation (involving adversarial samples) or model enhancement …

CascadePAIE: Reallocating Relevance for Event Roles and Event Text in Event Argument Extraction

2025

Event argument extraction is one of the subtasks in event extraction, and the current mainstream approaches define it as a span extraction task utilizing the concept of prompt learning. However, upon reproducing the current mainstream …