8 papers · 1 filter
Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attacks
Yaning Jia, Shenyang Deng, Yaoqing Yang +3
Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications, yet they remain highly vulnerable to adversarial attacks that maliciously perturb graph s…
Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
Tianyu Pang, Vignesh Kothapalli, Shenyang Deng +3
We study optimal learning-rate selection in two-layer and three-layer linear neural networks trained to learn linear target functions. In particular, we derive the exact closed-for…
RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization
Shenyang Deng, Zhuoli Ouyang, Tianyu Pang +4
Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information of the loss landscape. The central c…
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction
Tianyu Pang, Yujie Fang, Zihang Liu +4
Muon has recently shown promising results in LLM training. In this work, we study how to further improve Muon. We argue that Muon's orthogonalized update rule suppresses the emerge…
Depth, Not Data: An Analysis of Hessian Spectral Bifurcation
Shenyang Deng, Boyao Liao, Zhuoli Ouyang +2
The eigenvalue distribution of the Hessian matrix plays a crucial role in understanding the optimization landscape of deep neural networks. Prior work has attributed the well-docum…
Suspicious Alignment of SGD: A Fine-Grained Step Size Condition Analysis
Shenyang Deng, Boyao Liao, Zhuoli Ouyang +3
This paper explores the suspicious alignment phenomenon in stochastic gradient descent (SGD) under ill-conditioned optimization, where the Hessian spectrum splits into dominant and…