Search Results for author: Jun Zhou

Found 103 papers, 20 papers with code

RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language Learning

1 code implementation22 May 2022 Han Wang, Ruiliu Fu, Xuejun Zhang, Jun Zhou

In order to alleviate catastrophic forgetting, we propose the residual variational autoencoder (RVAE) to enhance LAMOL, a recent LLL model, by mapping different tasks into a limited unified semantic space.

KGNN: Distributed Framework for Graph Neural Knowledge Representation

no code implementations17 May 2022 Binbin Hu, Zhiyang Hu, Zhiqiang Zhang, Jun Zhou, Chuan Shi

Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services.

Link Prediction Representation Learning

Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings

no code implementations7 Apr 2022 Yuhao Mao, Chong Fu, Saizhuo Wang, Shouling Ji, Xuhong Zhang, Zhenguang Liu, Jun Zhou, Alex X. Liu, Raheem Beyah, Ting Wang

To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account.

Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective

1 code implementation21 Mar 2022 Jiawei Zhang, Xiang Wang, Xiao Bai, Chen Wang, Lei Huang, Yimin Chen, Lin Gu, Jun Zhou, Tatsuya Harada, Edwin R. Hancock

The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.

Contrastive Learning Stereo Matching

CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP

no code implementations15 Mar 2022 Mingjie Wang, Jun Zhou, Hao Cai, Minglun Gong

Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire.

Crowd Counting

Neural Graph Matching for Pre-training Graph Neural Networks

1 code implementation3 Mar 2022 Yupeng Hou, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou, Ji-Rong Wen

In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task.

Graph Matching

An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022

1 code implementation1 Mar 2022 Qian Zhao, Shuo Yang, Binbin Hu, Zhiqiang Zhang, Yakun Wang, Yusong Chen, Jun Zhou, Chuan Shi

Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area.

Graph Learning Link Prediction

Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift

no code implementations27 Jan 2022 Hongrui Liu, Binbin Hu, Xiao Wang, Chuan Shi, Zhiqiang Zhang, Jun Zhou

To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset.

Variational Inference

Robust Unsupervised Graph Representation Learning via Mutual Information Maximization

no code implementations21 Jan 2022 Jihong Wang, Minnan Luo, Jundong Li, Ziqi Liu, Jun Zhou, Qinghua Zheng

In particular, to quantify the robustness of GNNs without label information, we propose a robustness measure, named graph representation robustness (GRR), to evaluate the mutual information between adversarially perturbed node representations and the original graph.

Adversarial Attack Graph Representation Learning +1

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

1 code implementation28 Dec 2021 Boxin Zhao, Ziqi Liu, Chaochao Chen, Mladen Kolar, Zhiqiang Zhang, Jun Zhou

In this paper, we cast client sampling as an online learning task with bandit feedback, which we solve with an online stochastic mirror descent (OSMD) algorithm designed to minimize the sampling variance.

Federated Learning online learning +1

Benchmarking emergency department triage prediction models with machine learning and large public electronic health records

1 code implementation22 Nov 2021 Feng Xie, Jun Zhou, Jin Wee Lee, Mingrui Tan, Siqi Li, Logasan S/O Rajnthern, Marcel Lucas Chee, Bibhas Chakraborty, An-Kwok Ian Wong, Alon Dagan, Marcus Eng Hock Ong, Fei Gao, Nan Liu

In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400, 000 ED visits from 2011 to 2019.

MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data

no code implementations NeurIPS 2021 Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen, Jun Zhou, Jianyong Zhou

Time series forecasting is widely used in business intelligence, e. g., forecast stock market price, sales, and help the analysis of data trend.

Time Series Time Series Forecasting

Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable

1 code implementation Findings (EMNLP) 2021 Ruiliu Fu, Han Wang, Xuejun Zhang, Jun Zhou, Yonghong Yan

The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path.

PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method

no code implementations22 Oct 2021 Binchi Zhang, Minnan Luo, Shangbin Feng, Ziqi Liu, Jun Zhou, Qinghua Zheng

In light of these problems, we propose a Privacy-Preserving Subgraph sampling based distributed GCN training method (PPSGCN), which preserves data privacy and significantly cuts back on communication and memory overhead.

Federated Learning Graph Learning +1

Improving Generative Adversarial Networks via Adversarial Learning in Latent Space

no code implementations29 Sep 2021 Yang Li, Yichuan Mo, Liangliang Shi, Junchi Yan, Xiaolu Zhang, Jun Zhou

Although many efforts have been made in terms of backbone architecture design, loss function, and training techniques, few results have been obtained on how the sampling in latent space can affect the final performance, and existing works on latent space mainly focus on controllability.

A Unified Framework for Cross-Domain and Cross-System Recommendations

no code implementations18 Aug 2021 Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, Guanfeng Liu

Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i. e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively.

Graph Embedding

SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation

no code implementations18 Aug 2021 Kai Zhang, Hao Qian, Qi Liu, Zhiqiang Zhang, Jun Zhou, Jianhui Ma, Enhong Chen

Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.

Recommendation Systems

Improving Transferability of Adversarial Patches on Face Recognition with Generative Models

no code implementations CVPR 2021 Zihao Xiao, Xianfeng Gao, Chilin Fu, Yinpeng Dong, Wei Gao, Xiaolu Zhang, Jun Zhou, Jun Zhu

However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models.

Face Recognition

Improvement of Normal Estimation for PointClouds via Simplifying Surface Fitting

no code implementations21 Apr 2021 Jun Zhou, Wei Jin, Mingjie Wang, Xiuping Liu, Zhiyang Li, Zhaobin Liu

Firstly, a dynamic top-k selection strategy is introduced to better focus on the most critical points of a given patch, and the points selected by our learning method tend to fit a surface by way of a simple tangent plane, which can dramatically improve the normal estimation results of patches with sharp corners or complex patterns.

Fast and Accurate Normal Estimation for Point Cloud via Patch Stitching

no code implementations30 Mar 2021 Jun Zhou, Wei Jin, Mingjie Wang, Xiuping Liu, Zhiyang Li, Zhaobin Liu

At the stitching stage, we use the learned weights of multi-branch planar experts and distance weights between points to select the best normal from the overlapping parts.

Goal-Oriented Gaze Estimation for Zero-Shot Learning

1 code implementation CVPR 2021 Yang Liu, Lei Zhou, Xiao Bai, Yifei HUANG, Lin Gu, Jun Zhou, Tatsuya Harada

Therefore, we introduce a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization based on the class-level attributes for ZSL.

Gaze Estimation Generalized Zero-Shot Learning

Cross-Domain Recommendation: Challenges, Progress, and Prospects

no code implementations2 Mar 2021 Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, Guanfeng Liu

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain.

Recommendation Systems

STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting

no code implementations18 Dec 2020 Mingjie Wang, Hao Cai, XianFeng Han, Jun Zhou, Minglun Gong

To battle the ingrained issue of accuracy degradation, we propose a novel and powerful network called Scale Tree Network (STNet) for accurate crowd counting.

Crowd Counting

Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

no code implementations17 Dec 2020 Jun Zhou, Longfei Zheng, Chaochao Chen, Yan Wang, Xiaolin Zheng, Bingzhe Wu, Cen Chen, Li Wang, Jianwei Yin

In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.

Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction

no code implementations13 Dec 2020 Kai Zhang, Hao Qian, Qing Cui, Qi Liu, Longfei Li, Jun Zhou, Jianhui Ma, Enhong Chen

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.

Click-Through Rate Prediction

SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising

no code implementations3 Dec 2020 Fengchao Xiong, Shuyin Tao, Jun Zhou, Jianfeng Lu, Jiantao Zhou, Yuntao Qian

This model first projects the observed HSIs into a low-dimensional orthogonal subspace, and then represents the projected image with a multidimensional dictionary.

Hyperspectral Image Denoising Image Denoising

ASFGNN: Automated Separated-Federated Graph Neural Network

no code implementations6 Nov 2020 Longfei Zheng, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, Benyu Zhang

Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.

Multi-layer Feature Aggregation for Deep Scene Parsing Models

no code implementations4 Nov 2020 Litao Yu, Yongsheng Gao, Jun Zhou, Jian Zhang, Qiang Wu

The proposed module can auto-select the intermediate visual features to correlate the spatial and semantic information.

Scene Parsing Semantic Segmentation

Parameter Efficient Deep Neural Networks with Bilinear Projections

1 code implementation3 Nov 2020 Litao Yu, Yongsheng Gao, Jun Zhou, Jian Zhang

Recent research on deep neural networks (DNNs) has primarily focused on improving the model accuracy.

Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss

no code implementations22 Sep 2020 Cheng Yan, Guansong Pang, Xiao Bai, Jun Zhou, Lin Gu

The proposed loss is generic and can be used as a plugin to replace the triplet loss to significantly enhance different types of state-of-the-art approaches.

Person Re-Identification

PP-OCR: A Practical Ultra Lightweight OCR System

7 code implementations21 Sep 2020 Yuning Du, Chenxia Li, Ruoyu Guo, Xiaoting Yin, Weiwei Liu, Jun Zhou, Yifan Bai, Zilin Yu, Yehua Yang, Qingqing Dang, Haoshuang Wang

Meanwhile, several pre-trained models for the Chinese and English recognition are released, including a text detector (97K images are used), a direction classifier (600K images are used) as well as a text recognizer (17. 9M images are used).

Optical Character Recognition

Information Bottleneck Constrained Latent Bidirectional Embedding for Zero-Shot Learning

no code implementations16 Sep 2020 Yang Liu, Lei Zhou, Xiao Bai, Lin Gu, Tatsuya Harada, Jun Zhou

Though many ZSL methods rely on a direct mapping between the visual and the semantic space, the calibration deviation and hubness problem limit the generalization capability to unseen classes.

Zero-Shot Learning

3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation

no code implementations15 Sep 2020 Yuanpeng Liu, Jun Zhou, Yuqi Zhang, Chao Ding, Jun Wang

To address the problem, a novel 3DPVNet is presented in this work, which utilizes 3D local patches to vote for the object 6D poses.

6D Pose Estimation

A Comprehensive Analysis of Information Leakage in Deep Transfer Learning

no code implementations4 Sep 2020 Cen Chen, Bingzhe Wu, Minghui Qiu, Li Wang, Jun Zhou

To the best of our knowledge, our study is the first to provide a thorough analysis of the information leakage issues in deep transfer learning methods and provide potential solutions to the issue.

Transfer Learning

When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control

no code implementations20 Aug 2020 Chaochao Chen, Jun Zhou, Li Wang, Xibin Wu, Wenjing Fang, Jin Tan, Lei Wang, Alex X. Liu, Hao Wang, Cheng Hong

In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.

Bandit Samplers for Training Graph Neural Networks

2 code implementations NeurIPS 2020 Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi

However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).

Graph Attention

Topological Hall Effect and Skyrmion-like Bubbles at a Charge-transfer Interface

no code implementations3 Jun 2020 Zhi Shiuh Lim, Changjian Li, Zhen Huang, Xiao Chi, Jun Zhou, Shengwei Zeng, Ganesh Ji Omar, Yuan Ping Feng, Andrivo Rusydi, Stephen John Pennycook, Thirumalai Venkatesan, Ariando Ariando

Here, the emergence, tuning and interpretation of hump-shape Hall Effect from a CaMnO3/CaIrO3/CaMnO3 trilayer structure are studied in detail.

Mesoscale and Nanoscale Physics

Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting

no code implementations25 May 2020 Mingjie Wang, Hao Cai, Jun Zhou, Minglun Gong

Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images.

Crowd Counting

Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

no code implementations25 May 2020 Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.

Classification General Classification +1

Large-Scale Secure XGB for Vertical Federated Learning

no code implementations18 May 2020 Wenjing Fang, Derun Zhao, Jin Tan, Chaochao Chen, Chaofan Yu, Li Wang, Lei Wang, Jun Zhou, Benyu Zhang

Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.

Federated Learning

A Unified Weight Learning and Low-Rank Regression Model for Robust Complex Error Modeling

no code implementations10 May 2020 Miaohua Zhang, Yongsheng Gao, Jun Zhou

For the structured error caused by occlusions or disguises, we propose a GC function based rank approximation to measure the rank of error matrices.

Face Recognition Robust Face Recognition

Secret Sharing based Secure Regressions with Applications

no code implementations10 Apr 2020 Chaochao Chen, Liang Li, Wenjing Fang, Jun Zhou, Li Wang, Lei Wang, Shuang Yang, Alex Liu, Hao Wang

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.

Unpack Local Model Interpretation for GBDT

no code implementations3 Apr 2020 Wenjing Fang, Jun Zhou, Xiaolong Li, Kenny Q. Zhu

Besides the commonly used feature importance as a global interpretation, feature contribution is a local measure that reveals the relationship between a specific instance and the related output.

Feature Importance

NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay

no code implementations1 Apr 2020 Jianbin Lin, Zhiqiang Zhang, Jun Zhou, Xiaolong Li, Jingli Fang, Yanming Fang, Quan Yu, Yuan Qi

Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem.

Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

no code implementations12 Mar 2020 Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li

However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices.

Industrial Scale Privacy Preserving Deep Neural Network

no code implementations11 Mar 2020 Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei Wang, Jun Zhou, Shuang Yang

Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.

Fraud Detection

RNE: A Scalable Network Embedding for Billion-scale Recommendation

no code implementations10 Mar 2020 Jianbin Lin, Daixin Wang, Lu Guan, Yin Zhao, Binqiang Zhao, Jun Zhou, Xiaolong Li, Yuan Qi

However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.

Network Embedding

Generating Natural Language Adversarial Examples on a Large Scale with Generative Models

no code implementations10 Mar 2020 Yankun Ren, Jianbin Lin, Siliang Tang, Jun Zhou, Shuang Yang, Yuan Qi, Xiang Ren

It can attack text classification models with a higher success rate than existing methods, and provide acceptable quality for humans in the meantime.

Adversarial Text General Classification +3

SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks

no code implementations5 Mar 2020 Qitao Shi, Ya-Lin Zhang, Longfei Li, Xinxing Yang, Meng Li, Jun Zhou

Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems.

Feature Engineering

Practical Privacy Preserving POI Recommendation

no code implementations5 Mar 2020 Chaochao Chen, Jun Zhou, Bingzhe Wu, Wenjin Fang, Li Wang, Yuan Qi, Xiaolin Zheng

Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.

Federated Learning

Data-Free Adversarial Perturbations for Practical Black-Box Attack

no code implementations3 Mar 2020 ZhaoXin Huan, Yulong Wang, Xiaolu Zhang, Lin Shang, Chilin Fu, Jun Zhou

Adversarial examples often exhibit black-box attacking transferability, which allows that adversarial examples crafted for one model can fool another model.

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

1 code implementation28 Feb 2020 Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, Yuan Qi

Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.

Fraud Detection

DSSLP: A Distributed Framework for Semi-supervised Link Prediction

no code implementations27 Feb 2020 Dalong Zhang, Xianzheng Song, Ziqi Liu, Zhiqiang Zhang, Xin Huang, Lin Wang, Jun Zhou

Instead of training model on the whole graph, DSSLP is proposed to train on the \emph{$k$-hops neighborhood} of nodes in a mini-batch setting, which helps reduce the scale of the input graph and distribute the training procedure.

Link Prediction

Deep Residual-Dense Lattice Network for Speech Enhancement

1 code implementation27 Feb 2020 Mohammad Nikzad, Aaron Nicolson, Yongsheng Gao, Jun Zhou, Kuldip K. Paliwal, Fanhua Shang

Motivated by this, we propose the residual-dense lattice network (RDL-Net), which is a new CNN for speech enhancement that employs both residual and dense aggregations without over-allocating parameters for feature re-usage.

Speech Enhancement

Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing

no code implementations27 Feb 2020 Ziqi Liu, Dong Wang, Qianyu Yu, Zhiqiang Zhang, Yue Shen, Jian Ma, Wenliang Zhong, Jinjie Gu, Jun Zhou, Shuang Yang, Yuan Qi

In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.

Graph Representation Learning

Uncovering Insurance Fraud Conspiracy with Network Learning

no code implementations27 Feb 2020 Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi

In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.

Fraud Detection Graph Learning

How Much Can A Retailer Sell? Sales Forecasting on Tmall

no code implementations27 Feb 2020 Chaochao Chen, Ziqi Liu, Jun Zhou, Xiaolong Li, Yuan Qi, Yujing Jiao, Xingyu Zhong

By analyzing the data, we have two main observations, i. e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast).

Time Series Time Series Forecasting

Heterogeneous Graph Neural Networks for Malicious Account Detection

1 code implementation27 Feb 2020 Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song

We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform.

Secure Social Recommendation based on Secret Sharing

no code implementations6 Feb 2020 Chaochao Chen, Liang Li, Bingzhe Wu, Cheng Hong, Li Wang, Jun Zhou

It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems.

Recommendation Systems

Privacy Preserving PCA for Multiparty Modeling

no code implementations6 Feb 2020 Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang, Jun Zhou, Guiquan Liu, Shuang Yang

In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.

Fraud Detection

A Time Attention based Fraud Transaction Detection Framework

no code implementations26 Dec 2019 Longfei Li, Ziqi Liu, Chaochao Chen, Ya-Lin Zhang, Jun Zhou, Xiaolong Li

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security.

Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip

1 code implementation25 Nov 2019 Bo Wang, Jun Zhou, Weng-Fai Wong, Li-Shiuan Peh

We show that conventional artificial neural networks (ANN) such as multilayer perceptron, convolutional neural networks, as well as the latest residual neural networks can be mapped successfully onto Shenjing, realizing ANNs with SNN's energy efficiency.

Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selection

no code implementations18 Oct 2019 Jun Zhou, Hua Huang, Bin Liu, Xiuping Liu

Then we use multi-task optimization to train the normal estimation and local plane classification tasks simultaneously. Also, to integrate the advantages of multi-scale results, a scale selection strategy is adopted, which is a data-driven approach for selecting the optimal scale around each point and encourages subnetwork specialization.

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

no code implementations5 Oct 2019 Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan YAO, Guangyu Sun, Li Wang, Xiaolu Zhang, Jun Zhou

Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.

Generalization Bounds

Pruning from Scratch

1 code implementation27 Sep 2019 Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, Xiaolin Hu

Network pruning is an important research field aiming at reducing computational costs of neural networks.

Network Pruning

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

no code implementations NeurIPS 2019 Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.

TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial

no code implementations18 Jun 2019 Shaosheng Cao, Xinxing Yang, Cen Chen, Jun Zhou, Xiaolong Li, Yuan Qi

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business.

Fraud Detection

A One-step Pruning-recovery Framework for Acceleration of Convolutional Neural Networks

no code implementations18 Jun 2019 Dong Wang, Lei Zhou, Xiao Bai, Jun Zhou

Our method accelerates the network in one-step pruning-recovery manner with a novel optimization objective function, which achieves higher accuracy with much less cost compared with existing pruning methods.

Intrinsic Ferromagnetism in Electrenes

no code implementations10 Apr 2019 Jun Zhou, Yuan Ping Feng, Lei Shen

We report intrinsic ferromagnetism in monolayer electrides or electrenes, in which excess electrons act as anions.

Computational Physics Materials Science

Material Based Object Tracking in Hyperspectral Videos: Benchmark and Algorithms

no code implementations11 Dec 2018 Fengchao Xiong, Jun Zhou, Yuntao Qian

Traditional color images only depict color intensities in red, green and blue channels, often making object trackers fail in challenging scenarios, e. g., background clutter and rapid changes of target appearance.

Object Tracking

Wearable Affective Robot

no code implementations25 Oct 2018 Min Chen, Jun Zhou, Guangming Tao, Jun Yang, Long Hu

The learning algorithm for the life modeling embedded in Fitbot can achieve better user's experience of affective social interaction.

EEG Human-Computer Interaction

Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets

no code implementations2 Oct 2018 Mingjie Wang, Jun Zhou, Wendong Mao, Minglun Gong

To address this problem, a regularization method named Stochastic Feature Reuse is also presented.

Review Helpfulness Prediction with Embedding-Gated CNN

no code implementations29 Aug 2018 Cen Chen, Minghui Qiu, Yinfei Yang, Jun Zhou, Jun Huang, Xiaolong Li, Forrest Bao

Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions.

Dropout with Tabu Strategy for Regularizing Deep Neural Networks

no code implementations29 Aug 2018 Zongjie Ma, Abdul Sattar, Jun Zhou, Qingliang Chen, Kaile Su

Tabu Dropout has no extra parameters compared with the standard Dropout and also it is computationally cheap.

Design Identification of Curve Patterns on Cultural Heritage Objects: Combining Template Matching and CNN-based Re-Ranking

no code implementations17 May 2018 Jun Zhou, Yuhang Lu, Kang Zheng, Karen Smith, Colin Wilder, Song Wang

The goal of this paper is to address the challenging problem of automatically identifying the underlying full design of curve patterns from such a sherd.

Re-Ranking Template Matching

A Boosting Framework of Factorization Machine

no code implementations17 Apr 2018 Longfei Li, Peilin Zhao, Jun Zhou, Xiaolong Li

However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets.

Recommendation Systems

Feature Propagation on Graph: A New Perspective to Graph Representation Learning

no code implementations17 Apr 2018 Biao Xiang, Ziqi Liu, Jun Zhou, Xiaolong Li

In this paper, we first define the concept of feature propagation on graph formally, and then study its convergence conditions to equilibrium states.

Graph Embedding Graph Representation Learning

Distributed Collaborative Hashing and Its Applications in Ant Financial

no code implementations13 Apr 2018 Chaochao Chen, Ziqi Liu, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li

The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure.

Collaborative Filtering

Fast Subspace Clustering Based on the Kronecker Product

no code implementations15 Mar 2018 Lei Zhou, Xiao Bai, Xianglong Liu, Jun Zhou, Hancock Edwin

Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets.

Exploring Linear Relationship in Feature Map Subspace for ConvNets Compression

no code implementations15 Mar 2018 Dong Wang, Lei Zhou, Xueni Zhang, Xiao Bai, Jun Zhou

In this way, most of the representative information in the network can be retained in each cluster.

Time-sensitive Customer Churn Prediction based on PU Learning

no code implementations27 Feb 2018 Li Wang, Chaochao Chen, Jun Zhou, Xiaolong Li

With the fast development of Internet companies throughout the world, customer churn has become a serious concern.

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

3 code implementations3 Feb 2018 Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.

Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects

no code implementations7 Nov 2017 Yuhang Lu, Jun Zhou, Jing Wang, Jun Chen, Karen Smith, Colin Wilder, Song Wang

Motivated by the important archaeological application of exploring cultural heritage objects, in this paper we study the challenging problem of automatically segmenting curve structures that are very weakly stamped or carved on an object surface in the form of a highly noisy depth map.

Semantic Segmentation

Clothing Retrieval with Visual Attention Model

no code implementations31 Oct 2017 Zhonghao Wang, Yujun Gu, Ya zhang, Jun Zhou, Xiao Gu

The VAM is further connected to a global network to form an end-to-end network structure through Impdrop connection which randomly Dropout on the feature maps with the probabilities given by the attention map.


Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier

no code implementations12 Sep 2017 Zhiming Wang, Xiaolong Li, Jun Zhou

Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount.

Decision Making Small-Footprint Keyword Spotting +1

Attributing Hacks

1 code implementation7 Nov 2016 Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng, Jun Zhou

That is, given properties of sites and the temporal occurrence of attacks, we are able to attribute individual attacks to joint causes and vulnerabilities, as well as estimating the evolution of these vulnerabilities over time.

Identifying Designs from Incomplete, Fragmented Cultural Heritage Objects by Curve-Pattern Matching

no code implementations5 Aug 2016 Jun Zhou, Haozhou Yu, Karen Smith, Colin Wilder, Hongkai Yu, Song Wang

The challenge to reconstruct and study complete designs is stymied because 1) most fragmentary cultural-heritage objects contain only a small portion of the underlying full design, 2) in the case of a stamping application, the same design may be applied multiple times with spatial overlap on one object, and 3) curve patterns detected on an object are usually incomplete and noisy.

Cannot find the paper you are looking for? You can Submit a new open access paper.