1 code implementation • 17 Jun 2022 • Rui He, Yuanxi Sun, Youzeng Li, Zuwei Huang, Feng Hu, Xu Cheng, Jie Tang
In this paper, we apply Masked Autoencoders to improve algorithm performance on the GEBD tasks.
no code implementations • 30 May 2022 • Xu Cheng, Hao Zhang, Yue Xin, Wen Shen, Jie Ren, Quanshi Zhang
We also prove that adversarial training tends to strengthen the influence of unconfident input samples with large gradient norms in an exponential manner.
1 code implementation • 12 Mar 2022 • Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.
Ranked #1 on
Node Classification
on MAG-scholar-C
1 code implementation • 16 Feb 2022 • Zitong Yu, Chenxu Zhao, Kevin H. M. Cheng, Xu Cheng, Guoying Zhao
Can we train a unified model, and flexibly deploy it under various modality scenarios?
1 code implementation • 14 Jan 2022 • Ningyu Zhang, Xin Xie, Xiang Chen, Shumin Deng, Chuanqi Tan, Fei Huang, Xu Cheng, Huajun Chen
Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they struggle to reason rare or emerging unseen entities.
4 code implementations • 8 Dec 2021 • Chenhui Zhang, Yufei He, Yukuo Cen, Zhenyu Hou, Wenzheng Feng, Yuxiao Dong, Xu Cheng, Hongyun Cai, Feng He, Jie Tang
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #1 on
Node Property Prediction
on ogbn-mag
1 code implementation • NeurIPS 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
1 code implementation • 5 Nov 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang
This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i. e.} the view of multi-order interactions between input variables of DNNs.
no code implementations • 29 Sep 2021 • Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Xin Jin, Quanshi Zhang
This paper proposes a hypothesis to analyze the underlying reason for the cognitive difficulty of an image from two perspectives, i. e. a cognitive image usually makes a DNN strongly activated by cognitive concepts; discarding massive non-cognitive concepts may also help the DNN focus on cognitive concepts.
no code implementations • 31 Jul 2021 • Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Quanshi Zhang
This paper proposes a hypothesis for the aesthetic appreciation that aesthetic images make a neural network strengthen salient concepts and discard inessential concepts.
no code implementations • 21 Jun 2021 • Xu Cheng, Chuntung Chu, Yi Zheng, Jie Ren, Quanshi Zhang
In this paper, we rethink how a DNN encodes visual concepts of different complexities from a new perspective, i. e. the game-theoretic multi-order interactions between pixels in an image.
1 code implementation • 12 Mar 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
no code implementations • 6 Dec 2020 • Saul Ancari, Xu Cheng
We also give the upper and lower bounds for the bottom of the spectrum of the $L$-stability operator and discuss the $L$-stability of some special self-expanders.
Differential Geometry
no code implementations • 28 Oct 2020 • Hao Zhang, Xu Cheng, Yiting Chen, Quanshi Zhang
In this study, we define interaction components of different orders between two input variables based on game theory.
no code implementations • 13 Sep 2020 • Yushan Zhu, Wen Zhang, Mingyang Chen, Hui Chen, Xu Cheng, Wei zhang, Huajun Chen
In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.
no code implementations • 29 Jun 2020 • Die Zhang, Huilin Zhou, Hao Zhang, Xiaoyi Bao, Da Huo, Ruizhao Chen, Xu Cheng, Mengyue Wu, Quanshi Zhang
This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing.
no code implementations • 21 Jun 2020 • Hao Zhang, Yiting Chen, Haotian Ma, Xu Cheng, Qihan Ren, Liyao Xiang, Jie Shi, Quanshi Zhang
Compared to the traditional neural network, the RENN uses d-ary vectors/tensors as features, in which each element is a d-ary number.
no code implementations • CVPR 2020 • Xu Cheng, Zhefan Rao, Yilan Chen, Quanshi Zhang
Whereas, in the scenario of learning from raw data, the DNN learns visual concepts sequentially.
no code implementations • 9 Oct 2019 • Wenqiang Liu, Hongyun Cai, Xu Cheng, Sifa Xie, Yipeng Yu, Hanyu Zhang
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces.
no code implementations • 28 Aug 2019 • Wenqing Lin, Feng He, Faqiang Zhang, Xu Cheng, Hongyun Cai
Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification.
no code implementations • 18 Jul 2018 • Yuan Liu, Yuancheng Wang, Nan Li, Xu Cheng, Yifeng Zhang, Yongming Huang, Guojun Lu
We propose an attention-based approach to give a discrimination between texture areas and smooth areas.
no code implementations • 30 Nov 2017 • Song Yizhi, Xu Cheng, Ding Daoxin, Zhou Hang, Quan Tingwei, Li Shiwei
Convolution system is linear and time invariant, and can describe the optical imaging process.
no code implementations • 30 Oct 2013 • Suofei Zhang, Zhixin Sun, Xu Cheng, Zhenyang Wu
Despite the success of many advanced tracking methods in this area, tracking targets with drastic variation of appearance such as deformation, view change and partial occlusion in video sequences is still a challenge in practical applications.