activity
20182021
most citedRepresentation Learning for Attributed Multiplex Heterogeneous Network

479 citations · 588 across the 4 of their papers we have counts for

collaborators

9 papers

cs.LG20217 cited

Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

Qinkai Zheng, Xu Zou, Yuxiao Dong +5

Adversarial attacks on graphs have posed a major threat to the robustness of graph machine learning (GML) models. Naturally, there is an ever-escalating arms race between attackers…

cs.LG202186 cited

TDGIA:Effective Injection Attacks on Graph Neural Networks

Xu Zou, Qinkai Zheng, Yuxiao Dong +4

Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. However, recent studies have shown that GNNs are vulnerable to adversarial atta…

cs.CV2021

CogView: Mastering Text-to-Image Generation via Transformers

Ming Ding, Zhuoyi Yang, Wenyi Hong +8

Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4…

cs.CL2021

Controllable Generation from Pre-trained Language Models via Inverse Prompting

Xu Zou, Da Yin, Qingyang Zhong +4

Large-scale pre-trained language models have demonstrated strong capabilities of generating realistic text. However, it remains challenging to control the generation results. Previ…

cs.CL2021

M6: A Chinese Multimodal Pretrainer

Junyang Lin, Rui Men, An Yang +22

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1.9TB images and 292GB texts that cover a wide range of domains. We pro…

cs.IR202016 cited

Controllable Multi-Interest Framework for Recommendation

Yukuo Cen, Jianwei Zhang, Xu Zou +3

Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequent…