4 citations · 14 across the 19 of their papers we have counts for
6 papers · 1 filter
A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference
Shuaicheng Niu, Guohao Chen, Yaofo Chen +14
The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a…
Zeta: Dual Whitening for Matrix Optimization via Coordinate-Adaptive Preconditioning
Kaiwen Chen, Shuhai Zhang, Zimo Liu +7
Large-scale neural network training increasingly relies on matrix-aware optimizers that exploit the structure of weight parameters beyond element-wise adaptation. However, existing…
Deep Electromagnetic Structure Design Under Limited Evaluation Budgets
Shijian Zheng, Fangxiao Jin, Shuhai Zhang +2
Electromagnetic structure (EMS) design plays a critical role in developing advanced antennas and materials, but remains challenging due to high-dimensional design spaces and expens…
Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds
Shuhai Zhang, Jiahao Yang, Hui Luo +5
Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predic…
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score
Shuhai Zhang, Feng Liu, Jiahao Yang +4
Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimati…
Internal Wasserstein Distance for Adversarial Attack and Defense
Qicheng Wang, Shuhai Zhang, Jiezhang Cao +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks that would trigger misclassification of DNNs but may be imperceptible to human perception. Adversarial…