activity
20202024
most citedAchieving Adversarial Robustness via Sparsity

14 citations · 24 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases

Hang Yin, Liyao Xiang, Dong Ding +4

We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type…

cs.CR2023

Permutation Equivariance of Transformers and Its Applications

Hengyuan Xu, Liyao Xiang, Hangyu Ye +3

Revolutionizing the field of deep learning, Transformer-based models have achieved remarkable performance in many tasks. Recent research has recognized these models are robust to s…

cs.CV2021★ 6 cited

Robust and Accurate Object Detection via Self-Knowledge Distillation

Weipeng Xu, Pengzhi Chu, Renhao Xie +2

Object detection has achieved promising performance on clean datasets, but how to achieve better tradeoff between the adversarial robustness and clean precision is still under-expl…

cs.LG2021★ 3 cited

Privacy-Preserving Federated Learning on Partitioned Attributes

Shuang Zhang, Liyao Xiang, Xi Yu +4

Real-world data is usually segmented by attributes and distributed across different parties. Federated learning empowers collaborative training without exposing local data or model…

cs.LG2020★ 1 cited

Certified Distributional Robustness on Smoothed Classifiers

Jungang Yang, Liyao Xiang, Ruidong Chen +3

The robustness of deep neural networks (DNNs) against adversarial example attacks has raised wide attention. For smoothed classifiers, we propose the worst-case adversarial loss ov…

cs.LG2020★ 14 cited

Achieving Adversarial Robustness via Sparsity

Shufan Wang, Ningyi Liao, Liyao Xiang +2

Network pruning has been known to produce compact models without much accuracy degradation. However, how the pruning process affects a network's robustness and the working mechanis…