Search Results for author: Xu Zhang

Found 58 papers, 27 papers with code

Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering

no code implementations8 Mar 2023 Xu Zhang, Wenpeng Li, Yunfeng Shao, Yinchuan Li

data, we propose a clustered Bayesian FL model named cFedbayes by learning different prior distributions for different clients.

Bayesian Inference Federated Learning

Robust one-shot estimation over shared networks in the presence of denial-of-service attacks

1 code implementation28 Feb 2023 Xu Zhang, Marcos M. Vasconcelos

We consider the following scenario: multiple pairs of agents communicating strategically over shared communication networks in the presence of a jammer who may launch a denial-of-service.

Online Decomposition of Surface Electromyogram into Individual Motor Unit Activities Using Progressive FastICA Peel-off

no code implementations5 Jan 2023 Haowen Zhao, Xu Zhang, Maoqi Chen, Ping Zhou

For decomposing experimental SEMG data, the proposed online method was able to extract an average of 12. 00 +- 3. 46 MUs per trial, with a matching rate of 90. 38% compared with results from the expert-guided offline decomposition.

Surveillance Face Anti-spoofing

no code implementations3 Jan 2023 Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Chenxu Zhao, Xu Zhang, Stan Z. Li, Zhen Lei

In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks.

Contrastive Learning Face Anti-Spoofing +2

Efficient Visual Computing with Camera RAW Snapshots

1 code implementation15 Dec 2022 Zhihao LI, Ming Lu, Xu Zhang, Xin Feng, M. Salman Asif, Zhan Ma

Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP).

Autonomous Driving Image Compression +2

Avoiding spurious correlations via logit correction

1 code implementation2 Dec 2022 Sheng Liu, Xu Zhang, Nitesh Sekhar, Yue Wu, Prateek Singhal, Carlos Fernandez-Granda

Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels.

An Empirical Study of Automatic Post-Editing

no code implementations16 Sep 2022 Xu Zhang, Xiaojun Wan

In view of the importance of data augmentation in APE, we separately study the impact of the construction method of artificial corpora and artificial data domain on the performance of APE models.

Automatic Post-Editing Data Augmentation

CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose

no code implementations23 Jun 2022 Xu Zhang, Wen Wang, Zhe Chen, Yufei Xu, Jing Zhang, DaCheng Tao

Motivated by the progress of visual-language research, we propose that pre-trained language models (e. g., CLIP) can facilitate animal pose estimation by providing rich prior knowledge for describing animal keypoints in text.

Animal Pose Estimation Contrastive Learning

Personalized Federated Learning via Variational Bayesian Inference

no code implementations16 Jun 2022 Xu Zhang, Yinchuan Li, Wenpeng Li, Kaiyang Guo, Yunfeng Shao

Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients.

Bayesian Inference Personalized Federated Learning +1

Personalized Prompt for Sequential Recommendation

no code implementations19 May 2022 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xu Zhang, Leyu Lin, Qing He

Specifically, we build the personalized soft prefix prompt via a prompt generator based on user profiles and enable a sufficient training of prompts via a prompt-oriented contrastive learning with both prompt- and behavior-based augmentations.

Contrastive Learning Sequential Recommendation

Selective Fairness in Recommendation via Prompts

1 code implementation10 May 2022 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He

In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free.

Fairness Sequential Recommendation

Experimental quantum adversarial learning with programmable superconducting qubits

no code implementations4 Apr 2022 Wenhui Ren, Weikang Li, Shibo Xu, Ke Wang, Wenjie Jiang, Feitong Jin, Xuhao Zhu, Jiachen Chen, Zixuan Song, Pengfei Zhang, Hang Dong, Xu Zhang, Jinfeng Deng, Yu Gao, Chuanyu Zhang, Yaozu Wu, Bing Zhang, Qiujiang Guo, Hekang Li, Zhen Wang, Jacob Biamonte, Chao Song, Dong-Ling Deng, H. Wang

Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.

BIG-bench Machine Learning Quantum Machine Learning

Robust remote estimation over the collision channel in the presence of an intelligent jammer

1 code implementation1 Apr 2022 Xu Zhang, Marcos M. Vasconcelos

We consider a sensor-receiver pair communicating over a wireless channel in the presence of a jammer who may launch a denial-of-service attack.

Multi-view Multi-behavior Contrastive Learning in Recommendation

1 code implementation20 Mar 2022 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Qing He

We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user.

Contrastive Learning

SMDT: Selective Memory-Augmented Neural Document Translation

no code implementations5 Jan 2022 Xu Zhang, Jian Yang, Haoyang Huang, Shuming Ma, Dongdong Zhang, Jinlong Li, Furu Wei

Existing document-level neural machine translation (NMT) models have sufficiently explored different context settings to provide guidance for target generation.

Document Level Machine Translation Document Translation +3

Large-Scale Video Panoptic Segmentation in the Wild: A Benchmark

1 code implementation CVPR 2022 Jiaxu Miao, Xiaohan Wang, Yu Wu, Wei Li, Xu Zhang, Yunchao Wei, Yi Yang

In contrast, our large-scale VIdeo Panoptic Segmentation in the Wild (VIPSeg) dataset provides 3, 536 videos and 84, 750 frames with pixel-level panoptic annotations, covering a wide range of real-world scenarios and categories.

Panoptic Segmentation Video Panoptic Segmentation

Topological and Algebraic Structures of Atanassov's Intuitionistic Fuzzy-Values Space

no code implementations17 Nov 2021 Xinxing Wu, Tao Wang, Qian Liu, Peide Liu, Guanrong Chen, Xu Zhang

By introducing a new operator for IFVs via the linear order based on a score function and an accuracy function, we show that such an operator is a strong negation on IFVs.

V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing

no code implementations28 Oct 2021 Tianyue Zheng, Zhe Chen, Chao Cai, Jun Luo, Xu Zhang

Given the significant amount of time people spend in vehicles, health issues under driving condition have become a major concern.

Heart Rate Variability

Personalized Transfer of User Preferences for Cross-domain Recommendation

1 code implementation21 Oct 2021 Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, Qing He

Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user.

Recommendation Systems

Sparse Personalized Federated Learning

no code implementations12 Jul 2021 Xiaofeng Liu, Yinchuan Li, Qing Wang, Xu Zhang, Yunfeng Shao, Yanhui Geng

By incorporating an approximated L1-norm and the correlation between client models and global model into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with non-sparse FL.

Personalized Federated Learning

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

3 code implementations31 May 2021 Yongchun Zhu, Yudan Liu, Ruobing Xie, Fuzhen Zhuang, Xiaobo Hao, Kaikai Ge, Xu Zhang, Leyu Lin, Juan Cao

Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing.

Marketing Meta-Learning +1

Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

no code implementations11 May 2021 Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin, Qing He

With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage.

Meta-Learning Recommendation Systems

Optimal exit decision of venture capital under time-inconsistent preferences

no code implementations22 Mar 2021 Yanzhao Li, Ju'e Guo, Yongwu Li, Xu Zhang

Based on venture capitalists' understanding of future preferences, we consider four types of venture capitalists, namely time-consistent venture capitalists, venture capitalists who only realize critical time point inconsistency, naive venture capitalists and sophisticated venture capitalists, of which the latter three are time-inconsistent.

Understanding WeChat User Preferences and "Wow" Diffusion

1 code implementation4 Mar 2021 Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang, Xiao Liu, Ruobing Xie, Kai Zhuang, Xu Zhang, Leyu Lin, Philip S. Yu

"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.

Graph Representation Learning Social and Information Networks

UPRec: User-Aware Pre-training for Recommender Systems

no code implementations22 Feb 2021 Chaojun Xiao, Ruobing Xie, Yuan YAO, Zhiyuan Liu, Maosong Sun, Xu Zhang, Leyu Lin

Existing sequential recommendation methods rely on large amounts of training data and usually suffer from the data sparsity problem.

Self-Supervised Learning Sequential Recommendation

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation

1 code implementation LREC 2022 Wenhao Zhu, ShuJian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.

Autonomous Vehicles Domain Adaptation +3

Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction

no code implementations22 Dec 2020 Yao Zhang, Xu Zhang, Jun Wang, Hongru Liang, Wenqiang Lei, Zhe Sun, Adam Jatowt, Zhenglu Yang

The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.

Knowledge Graphs Link Prediction

Learning to Build User-tag Profile in Recommendation System (UTPM)

1 code implementation ACM International Conference on Information and Knowledge Management 2020 Su Yan, Xin Chen, Ran Huo, Xu Zhang, Leyu Lin

User profiling is one of the most important components in recommendation systems, where a user is profiled using demographic (e. g. gender, age, and location) and user behavior information (e. g. browsing and search history).

Multi-Label Classification Recommendation Systems +1

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

no code implementations17 Aug 2020 Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, Xun Chen

MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.

Arrhythmia Detection

Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds

no code implementations19 May 2020 Bo Xu, Xu Zhang, Zhixin Li, Matt Leotta, Shih-Fu Chang, Jie Shan

For points that belong to the same roof shape, a multi-cue, hierarchical RANSAC approach is proposed for efficient and reliable segmenting and reconstructing the building point cloud.

3D Reconstruction

Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation

no code implementations30 Mar 2020 Pei Zhang, Xu Zhang, Wei Chen, Jian Yu, Yan-Feng Wang, Deyi Xiong

In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.

Document Level Machine Translation Machine Translation +3

Detecting and Simulating Artifacts in GAN Fake Images

1 code implementation15 Jul 2019 Xu Zhang, Svebor Karaman, Shih-Fu Chang

By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.

GAN image forensics

Real-time Attention Based Look-alike Model for Recommender System

1 code implementation12 Jun 2019 Yudan Liu, Kaikai Ge, Xu Zhang, Leyu Lin

Recently, deep learning models play more and more important roles in contents recommender systems.

Recommendation Systems Representation Learning

Super Interaction Neural Network

1 code implementation29 May 2019 Yang Yao, Xu Zhang, Baile Xu, Furao Shen, Jian Zhao

Recent studies have demonstrated that the convolutional networks heavily rely on the quality and quantity of generated features.

Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning

no code implementations20 May 2019 Xu Zhang, Yang Yao, Baile Xu, Lekun Mao, Furao Shen, Jian Zhao, QIngwei Lin

In this paper, it is the first time to discuss the difficulty without support of old classes in class incremental learning, which is called as softmax suppression problem.

Class Incremental Learning Incremental Learning +1

Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

1 code implementation CVPR 2019 Mang Ye, Xu Zhang, Pong C. Yuen, Shih-Fu Chang

This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space.

Data Augmentation

Exact Controllability for a Refined Stochastic Wave Equation

1 code implementation18 Jan 2019 Qi Lü, Xu Zhang

By means of a new global Carleman estimate, we establish the exact controllability of our stochastic wave equation with three controls.

Optimization and Control 93B05, 60H15, 93B07, 35B45

FARSA: Fully Automated Roadway Safety Assessment

1 code implementation17 Jan 2019 Weilian Song, Scott Workman, Armin Hadzic, Xu Zhang, Eric Green, Mei Chen, Reginald Souleyrette, Nathan Jacobs

An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars).

Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning

no code implementations14 Jan 2019 Yi Zhen, Hang Chen, Xu Zhang, Meng Liu, Xin Meng, Jian Zhang, Jiantao Pu

To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology.

Optimal Feedback for Stochastic Linear Quadratic Control and Backward Stochastic Riccati Equations in Infinite Dimensions

1 code implementation4 Jan 2019 Qi Lu, Xu Zhang

It is a longstanding unsolved problem to characterize the optimal feedbacks for general SLQs (i. e., stochastic linear quadratic control problems) with random coefficients in infinite dimensions; while the same problem but in finite dimensions was just addressed in a recent work [36].

Optimization and Control Probability 60H15, 93E20, 60H25, 49J30

Second Order Optimality Conditions for Optimal Control Problems of Stochastic Evolution Equations

1 code implementation18 Nov 2018 Qi Lu, Haisen Zhang, Xu Zhang

In this paper, we establish some second order necessary/sufficient optimality conditions for optimal control problems of stochastic evolution equations in infinite dimensions.

Optimization and Control Primary 93E20, Secondary, 60H07, 60H15

SCMA based resource management of D2D communications for maximum sum-revenue

no code implementations12 Oct 2018 Linglin Kong, Li Ling, Xu Zhang

This problem is NP-hard, so we propose a heuristic algorithm based on semi-definite relaxation (SDR) programming to solve it.

Signal Processing

Heated-Up Softmax Embedding

1 code implementation ICLR 2019 Xu Zhang, Felix Xinnan Yu, Svebor Karaman, Wei zhang, Shih-Fu Chang

Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples.

Metric Learning

Tropical Principal Component Analysis and its Application to Phylogenetics

1 code implementation7 Oct 2017 Ruriko Yoshida, Leon Zhang, Xu Zhang

Principal component analysis is a widely-used method for the dimensionality reduction of a given data set in a high-dimensional Euclidean space.

Combinatorics Populations and Evolution

Learning Spread-out Local Feature Descriptors

2 code implementations ICCV 2017 Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang

We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors.

Learning Discriminative and Transformation Covariant Local Feature Detectors

1 code implementation CVPR 2017 Xu Zhang, Felix X. Yu, Svebor Karaman, Shih-Fu Chang

Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi by defining the concepts of "standard patch" and "canonical feature" and leverage these to train a novel robust covariant detector.

Image Retrieval

Learning discriminative and transformation covariant local feature detectors.

1 code implementation Computer Vision and Pattern Recognition 2017 Xu Zhang, Felix X. Yu, Svebor Karaman, Shih-Fu Chang

Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi [8] by defining the concepts of “standard patch” and “canonical feature” and leverage these to train a novel robust covariant detector.

Image Retrieval

Spatio-temporal Aware Non-negative Component Representation for Action Recognition

no code implementations27 Aug 2016 Jianhong Wang, Tian Lan, Xu Zhang, Limin Luo

This paper presents a novel mid-level representation for action recognition, named spatio-temporal aware non-negative component representation (STANNCR).

Action Recognition Temporal Action Localization

Fast Orthogonal Projection Based on Kronecker Product

no code implementations ICCV 2015 Xu Zhang, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar, Shengjin Wang, Shi-Fu Chang

We propose a family of structured matrices to speed up orthogonal projections for high-dimensional data commonly seen in computer vision applications.

Image Retrieval Quantization

Deep Transfer Network: Unsupervised Domain Adaptation

no code implementations2 Mar 2015 Xu Zhang, Felix Xinnan Yu, Shih-Fu Chang, Shengjin Wang

In this paper, we propose a new domain adaptation framework named Deep Transfer Network (DTN), where the highly flexible deep neural networks are used to implement such a distribution matching process.

Unsupervised Domain Adaptation

Efficient classification using parallel and scalable compressed model and Its application on intrusion detection

no code implementations14 May 2014 Tieming Chen, Xu Zhang, Shichao Jin, Okhee Kim

In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression.

Classification General Classification +2

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