Search Results for author: Hui Xiong

Found 65 papers, 24 papers with code

Detect Professional Malicious User with Metric Learning in Recommender Systems

no code implementations19 May 2022 Yuanbo Xu, Yongjian Yang, En Wang, Fuzhen Zhuang, Hui Xiong

2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem.

Metric Learning Outlier Detection +1

Reinforced Imitative Graph Learning for Mobile User Profiling

no code implementations13 Mar 2022 Dongjie Wang, Pengyang Wang, Yanjie Fu, Kunpeng Liu, Hui Xiong, Charles E. Hughes

The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities.

Graph Learning

MetAug: Contrastive Learning via Meta Feature Augmentation

1 code implementation10 Mar 2022 Jiangmeng Li, Wenwen Qiang, Changwen Zheng, Bing Su, Hui Xiong

We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder.

Contrastive Learning Informativeness +1

Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation

no code implementations8 Mar 2022 Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Bing Su, Hui Xiong

We conduct theoretical analysis on the robustness of the proposed RLPGA and prove that the robust informative-theoretic-based loss and the local preserving module are beneficial to reduce the empirical risk of the target domain.

Unsupervised Domain Adaptation

Hyperbolic Graph Neural Networks: A Review of Methods and Applications

1 code implementation28 Feb 2022 Menglin Yang, Min Zhou, Zhihao LI, Jiahong Liu, Lujia Pan, Hui Xiong, Irwin King

Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.

Graph Learning

Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams

no code implementations19 Jan 2022 Dongjie Wang, Kunpeng Liu, Hui Xiong, Yanjie Fu

An event that a user visits a POI in stream updates the states of both users and geospatial contexts; the agent perceives the updated environment state to make online recommendations.

reinforcement-learning

Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He

Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users' dynamic preferences.

Learning to Walk with Dual Agents for Knowledge Graph Reasoning

1 code implementation23 Dec 2021 Denghui Zhang, Zixuan Yuan, Hao liu, Xiaodong Lin, Hui Xiong

Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge graph (KG) by exploring multi-hop relational paths.

reinforcement-learning

Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis

no code implementations2 Dec 2021 Zixuan Yuan, Yada Zhu, Wei zhang, Ziming Huang, Guangnan Ye, Hui Xiong

Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals.

Data Augmentation

Topic Modeling Revisited: A Document Graph-based Neural Network Perspective

1 code implementation NeurIPS 2021 Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, HengShu Zhu, Hui Xiong

To this end, in this paper, we revisit the task of topic modeling by transforming each document into a directed graph with word dependency as edges between word nodes, and develop a novel approach, namely Graph Neural Topic Model (GNTM).

Variational Inference

Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment

no code implementations1 Dec 2021 Denghui Zhang, Zixuan Yuan, Yanchi Liu, Hao liu, Fuzhen Zhuang, Hui Xiong, Haifeng Chen

Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.

Entity Alignment Natural Language Processing

Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

1 code implementation NeurIPS 2021 Ying Sun, HengShu Zhu, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong

To this end, in this paper, we aim to discern the decision-making processes of neural networks through a hierarchical voting strategy by developing an explainable deep learning model, namely Voting Transformation-based Explainable Neural Network (VOTEN).

Decision Making

Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness

1 code implementation24 Oct 2021 Dazhong Shen, Chuan Qin, Chao Wang, HengShu Zhu, Enhong Chen, Hui Xiong

As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference.

Variational Inference

Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning

no code implementations22 Oct 2021 Yang Yang, Hongchen Wei, HengShu Zhu, dianhai yu, Hui Xiong, Jian Yang

In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty of using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) Prediction consistency.

Image Captioning Informativeness

Domain-Invariant Representation Learning with Global and Local Consistency

no code implementations29 Sep 2021 Wenwen Qiang, Jiangmeng Li, Jie Hu, Bing Su, Changwen Zheng, Hui Xiong

In this paper, we give an analysis of the existing representation learning framework of unsupervised domain adaptation and show that the learned feature representations of the source domain samples are with discriminability, compressibility, and transferability.

Representation Learning Unsupervised Domain Adaptation

GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

no code implementations24 Sep 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Dejing Dou, Hui Xiong

Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs.

Contrastive Learning Data Augmentation +2

Adversarial Neural Trip Recommendation

no code implementations24 Sep 2021 Linlang Jiang, Jingbo Zhou, Tong Xu, Yanyan Li, Hao Chen, Jizhou Huang, Hui Xiong

To that end, we propose an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above challenges.

Recommendation Systems

Information Theory-Guided Heuristic Progressive Multi-View Coding

no code implementations6 Sep 2021 Jiangmeng Li, Wenwen Qiang, Hang Gao, Bing Su, Farid Razzak, Jie Hu, Changwen Zheng, Hui Xiong

To this end, we rethink the existing multi-view learning paradigm from the information theoretical perspective and then propose a novel information theoretical framework for generalized multi-view learning.

Contrastive Learning MULTI-VIEW LEARNING +1

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

1 code implementation21 Jul 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Liang Huang, Fan Wang, Haoyi Xiong, Weili Huang, Dejing Dou, Hui Xiong

To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).

Drug Discovery Graph Attention

MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

no code implementations12 Jul 2021 Weijia Zhang, Hao liu, Lijun Zha, HengShu Zhu, Ji Liu, Dejing Dou, Hui Xiong

Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e. g., real estate agents, appraisers, lenders, and buyers).

Decision Making Graph Representation Learning +1

Deep Subdomain Adaptation Network for Image Classification

1 code implementation17 Jun 2021 Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He

The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.

Classification Domain Adaptation +4

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

2 code implementations14 Jun 2021 Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu

In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.

Anomaly Detection

Heterogeneous Graph Representation Learning with Relation Awareness

1 code implementation24 May 2021 Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Weifeng Lv, Hui Xiong

Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations.

Graph Learning Graph Representation Learning +2

Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning

1 code implementation15 Feb 2021 Weijia Zhang, Hao liu, Fan Wang, Tong Xu, Haoran Xin, Dejing Dou, Hui Xiong

Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability.

Multi-agent Reinforcement Learning reinforcement-learning

Out-of-Town Recommendation with Travel Intention Modeling

1 code implementation29 Jan 2021 Haoran Xin, Xinjiang Lu, Tong Xu, Hao liu, Jingjing Gu, Dejing Dou, Hui Xiong

Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM).

point of interests

CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation

no code implementations28 Jan 2021 Carter Blum, Hao liu, Hui Xiong

Electric vehicles have been rapidly increasing in usage, but stations to charge them have not always kept up with demand, so efficient routing of vehicles to stations is critical to operating at maximum efficiency.

Decision Making Q-Learning +1

Spatial Object Recommendation with Hints: When Spatial Granularity Matters

no code implementations8 Jan 2021 Hui Luo, Jingbo Zhou, Zhifeng Bao, Shuangli Li, J. Shane Culpepper, Haochao Ying, Hao liu, Hui Xiong

We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.

Multi-Task Learning Representation Learning

Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks

no code implementations30 Dec 2020 Jindong Han, Hao liu, HengShu Zhu, Hui Xiong, Dejing Dou

Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations.

Graph Learning Multi-Task Learning

Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning

1 code implementation29 Dec 2020 Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Weifeng Lv, Hui Xiong

Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information.

Graph Learning Representation Learning

Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction

1 code implementation17 Dec 2020 Jingbo Zhou, Shuangli Li, Liang Huang, Haoyi Xiong, Fan Wang, Tong Xu, Hui Xiong, Dejing Dou

The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.

Drug Discovery Graph Attention +1

Coupled Layer-wise Graph Convolution for Transportation Demand Prediction

1 code implementation15 Dec 2020 Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Hui Xiong

Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands.

T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting

no code implementations26 Oct 2020 Denghui Zhang, Yanchi Liu, Wei Cheng, Bo Zong, Jingchao Ni, Zhengzhang Chen, Haifeng Chen, Hui Xiong

Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.

Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop

no code implementations2 Oct 2020 Wei Fan, Kunpeng Liu, Hao liu, Yong Ge, Hui Xiong, Yanjie Fu

In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF).

Feature Importance feature selection +1

Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning

1 code implementation16 Sep 2020 Denghui Zhang, Junming Liu, HengShu Zhu, Yanchi Liu, Lichen Wang, Pengyang Wang, Hui Xiong

However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e. g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns.

Link Prediction Representation Learning

E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce

no code implementations7 Sep 2020 Denghui Zhang, Zixuan Yuan, Yanchi Liu, Fuzhen Zhuang, Haifeng Chen, Hui Xiong

Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.

Aspect Extraction Denoising +4

Learning Adaptive Embedding Considering Incremental Class

no code implementations31 Aug 2020 Yang Yang, Zhen-Qiang Sun, HengShu Zhu, Yanjie Fu, Hui Xiong, Jian Yang

To this end, we propose a Class-Incremental Learning without Forgetting (CILF) framework, which aims to learn adaptive embedding for processing novel class detection and model update in a unified framework.

class-incremental learning Incremental Learning

S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification

no code implementations11 Aug 2020 Yang Yang, Zhen-Qiang Sun, Hui Xiong, Jian Yang

Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time.

General Classification Knowledge Distillation

Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine

no code implementations11 Jul 2020 Hao Liu, Ying Li, Yanjie Fu, Huaibo Mei, Jingbo Zhou, Xu Ma, Hui Xiong

Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.

Predicting Temporal Sets with Deep Neural Networks

2 code implementations20 Jun 2020 Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Hui Xiong, Weifeng Lv

Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set.

Time Series

Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems

1 code implementation13 Apr 2020 Jingjing Gu, Qiang Zhou, Jingyuan Yang, Yanchi Liu, Fuzhen Zhuang, Yanchao Zhao, Hui Xiong

Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility.

A Survey on Knowledge Graph-Based Recommender Systems

no code implementations28 Feb 2020 Qingyu Guo, Fuzhen Zhuang, Chuan Qin, HengShu Zhu, Xing Xie, Hui Xiong, Qing He

On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.

Recommendation Systems

SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

1 code implementation23 Feb 2020 Chao Wang, HengShu Zhu, Chen Zhu, Chuan Qin, Hui Xiong

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases.

Collaborative Ranking Recommendation Systems

Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling

no code implementations8 Feb 2020 Mu Yuan, Lan Zhang, Xiang-Yang Li, Hui Xiong

With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e. g., the number of high-confidence labels).

Image Retrieval

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

1 code implementation24 Nov 2019 Weijia Zhang, Hao liu, Yanchi Liu, Jingbo Zhou, Hui Xiong

However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).

A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring

no code implementations21 Nov 2019 Dazhong Shen, Qi Zhang, Tong Xu, HengShu Zhu, Wenjia Zhao, Zikai Yin, Peilun Zhou, Lihua Fang, Enhong Chen, Hui Xiong

To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms.

Ensemble Learning

A Comprehensive Survey on Transfer Learning

2 code implementations7 Nov 2019 Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, HengShu Zhu, Hui Xiong, Qing He

In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.

Transfer Learning

STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control

no code implementations28 Aug 2019 Yanan Wang, Tong Xu, Xin Niu, Chang Tan, Enhong Chen, Hui Xiong

Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method.

Multi-agent Reinforcement Learning Q-Learning +1

EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction

1 code implementation7 Jun 2019 Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, Guoping Hu

In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content.

Knowledge Tracing

Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation

no code implementations29 May 2019 Jian Liu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Fuzheng Zhuang, Jiajie Xu, Xiaofang Zhou, Hui Xiong

Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences.

Transfer Learning

POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm

no code implementations1 May 2019 Jinyin Chen, Mengmeng Su, Shijing Shen, Hui Xiong, Haibin Zheng

In this paper, comprehensive evaluation metrics are brought up for different adversarial attack methods.

Adversarial Attack

Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach

no code implementations21 Dec 2018 Chuan Qin, HengShu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, Hui Xiong

The wide spread use of online recruitment services has led to information explosion in the job market.

FineFool: Fine Object Contour Attack via Attention

no code implementations1 Dec 2018 Jinyin Chen, Haibin Zheng, Hui Xiong, Mengmeng Su

Inspired by the correlations between adversarial perturbations and object contour, slighter perturbations is produced via focusing on object contour features, which is more imperceptible and difficult to be defended, especially network add-on defense methods with the trade-off between perturbations filtering and contour feature loss.

Adversarial Attack

Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning

no code implementations8 Oct 2018 Chen Zhu, HengShu Zhu, Hui Xiong, Chao Ma, Fang Xie, Pengliang Ding, Pan Li

To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job.

Data Visualization Representation Learning

Risk-Averse Classification

no code implementations30 Apr 2018 Constantine Vitt, Darinka Dentcheva, Hui Xiong

We develop a new approach to solving classification problems, which is bases on the theory of coherent measures of risk and risk sharing ideas.

Classification General Classification

Recruitment Market Trend Analysis with Sequential Latent Variable Models

no code implementations8 Dec 2017 Chen Zhu, HengShu Zhu, Hui Xiong, Pengliang Ding, Fang Xie

To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data.

REMIX: Automated Exploration for Interactive Outlier Detection

no code implementations17 May 2017 Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong

This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors.

Meta-Learning Outlier Detection

Dynamic Word Embeddings for Evolving Semantic Discovery

4 code implementations2 Mar 2017 Zijun Yao, Yifan Sun, Weicong Ding, Nikhil Rao, Hui Xiong

Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution.

Word Embeddings

Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling

no code implementations25 Jul 2016 Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric Xing, Jieping Ye

Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.

Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval

no code implementations25 Sep 2014 Liang Wu, Hui Xiong, Liang Du, Bo Liu, Guandong Xu, Yong Ge, Yanjie Fu, Yuanchun Zhou, Jianhui Li

Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.

Information Retrieval Metric Learning

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