1 citations · 1 across the 5 of their papers we have counts for
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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,…
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…
SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds
Wuxinlin Cheng, Chenhui Deng, Ali Aghdaei +2
Modern graph neural networks (GNNs) can be sensitive to changes in the input graph structure and node features, potentially resulting in unpredictable behavior and degraded perform…
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
Wuxinlin Cheng, Chenhui Deng, Zhiqiang Zhao +3
A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model. Our approach, named SPADE, exploits bijective distance m…