no code implementations • EMNLP 2021 • Zeru Zhang, Zijie Zhang, Yang Zhou, Lingfei Wu, Sixing Wu, Xiaoying Han, Dejing Dou, Tianshi Che, Da Yan
Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks.
no code implementations • 22 Jun 2022 • Jiayin Jin, Zeru Zhang, Yang Zhou, Lingfei Wu
Theoretical analysis is conducted to derive that the Nemytskii operator is smooth and induces a Frechet differentiable smooth manifold.
no code implementations • NeurIPS 2021 • Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, Dejing Dou
Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks.
1 code implementation • 16 Apr 2021 • Gong Zhang, Yang Zhou, Sixing Wu, Zeru Zhang, Dejing Dou
With the guidance of known aligned entities in the context of multiple random walks, an adversarial knowledge translation model is developed to fill and translate masked entities in pairwise random walks from two KGs.
no code implementations • NeurIPS 2020 • Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou
Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.