Search Results for author: Xu Cheng

Found 23 papers, 8 papers with code

Why Adversarial Training of ReLU Networks Is Difficult?

no code implementations30 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.

GRAND+: Scalable Graph Random Neural Networks

1 code implementation12 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.

Data Augmentation Graph Learning +1

Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings

1 code implementation14 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.

Knowledge Graph Embedding Knowledge Graph Embeddings +1

SCR: Training Graph Neural Networks with Consistency Regularization

4 code implementations8 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.

Node Classification

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness

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.

Adversarial Robustness

A Unified Game-Theoretic Interpretation of Adversarial Robustness

1 code implementation5 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.

Adversarial Robustness

A HYPOTHESIS FOR THE COGNITIVE DIFFICULTY OF IMAGES

no code implementations29 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.

A Hypothesis for the Aesthetic Appreciation in Neural Networks

no code implementations31 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.

A Game-Theoretic Taxonomy of Visual Concepts in DNNs

no code implementations21 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.

A Unified Game-Theoretic Interpretation of Adversarial Robustness

1 code implementation12 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.

Adversarial Robustness

Volume properties and rigidity on self-expanders of mean curvature flow

no code implementations6 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

Technical Note: Game-Theoretic Interactions of Different Orders

no code implementations28 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.

DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning

no code implementations13 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.

Knowledge Distillation Knowledge Graph Embedding +2

Building Interpretable Interaction Trees for Deep NLP Models

no code implementations29 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.

Natural Language Processing

Rotation-Equivariant Neural Networks for Privacy Protection

no code implementations21 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.

Initialization for Network Embedding: A Graph Partition Approach

no code implementations28 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.

General Classification graph partitioning +3

Tracking Deformable Parts via Dynamic Conditional Random Fields

no code implementations30 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.

object-detection Object Detection +2

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