3 citations · 3 across the 9 of their papers we have counts for
9 papers
HyperEF 2.0: Spectral Hypergraph Coarsening via Krylov Subspace Expansion and Resistance-based Local Clustering
Hamed Sajadinia, Zhuo Feng
This paper introduces HyperEF 2.0, a scalable framework for spectral coarsening and clustering of large-scale hypergraphs through hyperedge effective resistances, aiming to decompo…
SALMAN: Stability Analysis of Language Models Through the Maps Between Graph-based Manifolds
Wuxinlin Cheng, Yupeng Cao, Jinwen Wu +3
Recent strides in pretrained transformer-based language models have propelled state-of-the-art performance in numerous NLP tasks. Yet, as these models grow in size and deployment,…
dyGRASS: Dynamic Spectral Graph Sparsification via Localized Random Walks on GPUs
Yihang Yuan, Ali Aghdaei, Zhuo Feng
This work presents dyGRASS, an efficient dynamic algorithm for spectral sparsification of large undirected graphs that undergo streaming edge insertions and deletions. At its core,…
SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioning
Hamed Sajadinia, Ali Aghdaei, Zhuo Feng
State-of-the-art hypergraph partitioners utilize a multilevel paradigm to construct progressively coarser hypergraphs across multiple layers, guiding cut refinements at each level…
SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks
John Anticev, Ali Aghdaei, Wuxinlin Cheng +1
SGM-PINN is a graph-based importance sampling framework to improve the training efficacy of Physics-Informed Neural Networks (PINNs) on parameterized problems. By applying a graph…
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning
Tao Liu, Yuhang Zhang, Zhu Feng +4
Backdoors on federated learning will be diluted by subsequent benign updates. This is reflected in the significant reduction of attack success rate as iterations increase, ultimate…