Search Results for author: Philip S. Yu

Found 206 papers, 83 papers with code

H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network

no code implementations EMNLP 2020 Shen Wang, Xiaokai Wei, Cicero Nogueira dos santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S. Yu

Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.

Graph Attention Knowledge Graph Embedding +2

Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction

no code implementations14 Sep 2021 Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, Philip S. Yu

Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.

Meta-Learning Relation Extraction +1

Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

no code implementations7 Sep 2021 Haoran Yang, Hongxu Chen, Lin Li, Philip S. Yu, Guandong Xu

They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.

Contrastive Learning Multi-Task Learning +1

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

no code implementations26 Aug 2021 Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, Philip S. Yu

In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.

Knowledge Graphs Recommendation Systems

Contrastive Self-supervised Sequential Recommendation with Robust Augmentation

1 code implementation14 Aug 2021 Zhiwei Liu, Yongjun Chen, Jia Li, Philip S. Yu, Julian McAuley, Caiming Xiong

In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.

Contrastive Learning Self-Supervised Learning

Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

1 code implementation14 Aug 2021 Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, Philip S. Yu

Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging.

A Comprehensive Survey on Schema-based Event Extraction with Deep Learning

no code implementations5 Jul 2021 Qian Li, Hao Peng, JianXin Li, Yiming Hei, Rui Sun, Jiawei Sheng, Shu Guo, Lihong Wang, Jia Wu, Amin Beheshti, Philip S. Yu

Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

Event Extraction

Dual Adversarial Variational Embedding for Robust Recommendation

no code implementations30 Jun 2021 Qiaomin Yi, Ning Yang, Philip S. Yu

First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns.

Variational Inference

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

1 code implementation23 Jun 2021 Qian Li, Hao Peng, JianXin Li, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang

Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.

Event Extraction Incremental Learning

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

1 code implementation23 Jun 2021 Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu

We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data.

Data Augmentation Representation Learning

Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation

1 code implementation11 Jun 2021 Ziwei Fan, Zhiwei Liu, Lei Zheng, Shen Wang, Philip S. Yu

We use Elliptical Gaussian distributions to describe items and sequences with uncertainty.

Sketch-Based Streaming Anomaly Detection in Dynamic Graphs

1 code implementation8 Jun 2021 Siddharth Bhatia, Mohit Wadhwa, Philip S. Yu, Bryan Hooi

This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).

Anomaly Detection Intrusion Detection

A Comprehensive Survey on Community Detection with Deep Learning

1 code implementation26 May 2021 Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu

A community reveals the features and connections of its members that are different from those in other communities in a network.

Community Detection Graph Attention +2

A Robust and Generalized Framework for Adversarial Graph Embedding

1 code implementation22 May 2021 JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He

With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.

Graph Embedding Graph Mining +3

Graph Learning based Recommender Systems: A Review

1 code implementation13 May 2021 Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).

Graph Learning Recommendation Systems

Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer

1 code implementation2 May 2021 Zhiwei Liu, Ziwei Fan, Yu Wang, Philip S. Yu

We firstly pre-train a transformer with sequences in a reverse direction to predict prior items.

User Preference-aware Fake News Detection

1 code implementation25 Apr 2021 Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun

The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.

Fact Checking Fake News Detection +2

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

1 code implementation16 Apr 2021 JianXin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He

Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.

Node Classification Node Clustering +1

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

1 code implementation16 Apr 2021 Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, Philip S. Yu

A reinforced relation-aware neighbor selection mechanism is developed to choose the most similar neighbors of a targeting node within a relation before aggregating all neighborhood information from different relations to obtain the eventual node embedding.

Node Classification Representation Learning

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Molecular Property Prediction

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

1 code implementation NAACL 2021 Zhongfen Deng, Hao Peng, Dongxiao He, JianXin Li, Philip S. Yu

The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification.

General Classification Representation Learning +1

Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs

no code implementations6 Apr 2021 Li Sun, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su, Philip S. Yu

To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.

Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

1 code implementation2 Apr 2021 Hao Peng, JianXin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, Lifang He

Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.

Event Detection

I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications

no code implementations30 Mar 2021 Nooshin Mojab, Vahid Noroozi, Abdullah Aleem, Manoj P. Nallabothula, Joseph Baker, Dimitri T. Azar, Mark Rosenblatt, RV Paul Chan, Darvin Yi, Philip S. Yu, Joelle A. Hallak

In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings.

An Introduction to Robust Graph Convolutional Networks

no code implementations27 Mar 2021 Mehrnaz Najafi, Philip S. Yu

In this paper, we propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data where data may come from multiple sources.

Word Embeddings

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning

1 code implementation17 Mar 2021 Yunbo Wang, Haixu Wu, Jianjin Zhang, Zhifeng Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long

This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment.

Video Prediction

BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism

1 code implementation10 Mar 2021 Zi-Yuan Hu, Jin Huang, Zhi-Hong Deng, Chang-Dong Wang, Ling Huang, Jian-Huang Lai, Philip S. Yu

Representation learning tries to learn a common low dimensional space for the representations of users and items.

Representation Learning

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

Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation

no code implementations22 Jan 2021 Ye Liu, Yao Wan, Jian-Guo Zhang, Wenting Zhao, Philip S. Yu

In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance.

Machine Translation

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

2 code implementations21 Jan 2021 Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, JianXin Li, Philip S. Yu

The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.

Event Detection Feature Engineering +3

Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning

no code implementations19 Jan 2021 Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, Zeng Zeng

We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

no code implementations19 Jan 2021 Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, Zeng Zeng

The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.

Heterogeneous Similarity Graph Neural Network on Electronic Health Records

no code implementations17 Jan 2021 Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, Philip S. Yu

EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.

Dynamic Graph Collaborative Filtering

1 code implementation8 Jan 2021 Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, Philip S. Yu

Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.

Recommendation Systems

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

no code implementations3 Jan 2021 Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang

We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

Community Detection

Privacy and Robustness in Federated Learning: Attacks and Defenses

no code implementations7 Dec 2020 Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu

Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.

Federated Learning

A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

no code implementations30 Nov 2020 Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu

Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e. g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years.

Graph Classification Graph Embedding +4

Deoscillated Graph Collaborative Filtering

1 code implementation4 Nov 2020 Zhiwei Liu, Lin Meng, Fei Jiang, Jiawei Zhang, Philip S. Yu

Stacking multiple cross-hop propagation layers and locality layers constitutes the DGCF model, which models high-order CF signals adaptively to the locality of nodes and layers.

Recommendation Systems

Understanding Pre-trained BERT for Aspect-based Sentiment Analysis

1 code implementation COLING 2020 Hu Xu, Lei Shu, Philip S. Yu, Bing Liu

Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context.

Aspect-Based Sentiment Analysis Language Modelling +1

Basket Recommendation with Multi-Intent Translation Graph Neural Network

1 code implementation22 Oct 2020 Zhiwei Liu, Xiaohan Li, Ziwei Fan, Stephen Guo, Kannan Achan, Philip S. Yu

The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket.

Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

no code implementations13 Oct 2020 Yue Wang, Zhuo Xu, Lu Bai, Yao Wan, Lixin Cui, Qian Zhao, Edwin R. Hancock, Philip S. Yu

To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.

Event Extraction

Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

1 code implementation Findings of the Association for Computational Linguistics 2020 Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip S. Yu

Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components.

Few-Shot Learning Generalized Few-Shot Learning +1

Semi-supervised Relation Extraction via Incremental Meta Self-Training

1 code implementation6 Oct 2020 Xuming Hu, Chenwei Zhang, Fukun Ma, Chenyao Liu, Lijie Wen, Philip S. Yu

To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples.

Meta-Learning Relation Classification

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

no code implementations COLING 2020 Lichao Sun, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu, Lifang He

Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.

Data Augmentation Image Classification

Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score

1 code implementation28 Sep 2020 He Huang, Shunta Saito, Yuta Kikuchi, Eiichi Matsumoto, Wei Tang, Philip S. Yu

Motivated by the fact that detecting these rare relations can be critical in real-world applications, this paper introduces a novel integrated framework of classification and ranking to resolve the class imbalance problem in scene graph parsing.

KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

1 code implementation26 Sep 2020 Ye Liu, Yao Wan, Lifang He, Hao Peng, Philip S. Yu

To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.

Graph Attention Text Generation

Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination

no code implementations25 Sep 2020 Tao Zhang, Tianqing Zhu, Jing Li, Mengde Han, Wanlei Zhou, Philip S. Yu

A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.

Ensemble Learning Fairness

Fairness Constraints in Semi-supervised Learning

no code implementations14 Sep 2020 Tao Zhang, Tianqing Zhu, Mengde Han, Jing Li, Wanlei Zhou, Philip S. Yu

Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.


Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

no code implementations30 Aug 2020 Qingyun Sun, Hao Peng, Jian-Xin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, Lifang He

Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.

Graph Embedding

Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency

2 code implementations24 Aug 2020 Youwei Liang, Dong Huang, Chang-Dong Wang, Philip S. Yu

To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and multi-view inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned.

Graph Learning

Differentially Private Multi-Agent Planning for Logistic-like Problems

no code implementations16 Aug 2020 Dayong Ye, Tianqing Zhu, Sheng Shen, Wanlei Zhou, Philip S. Yu

To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems.

Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market

no code implementations12 Aug 2020 Hao Peng, Jian-Xin Li, Zheng Wang, Renyu Yang, Mingzhe Liu, Mingming Zhang, Philip S. Yu, Lifang He

As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation.

Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering

no code implementations6 Aug 2020 Ye Liu, Shaika Chowdhury, Chenwei Zhang, Cornelia Caragea, Philip S. Yu

Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge.

Question Answering

Data science and AI in FinTech: An overview

no code implementations10 Jul 2020 Longbing Cao, Qiang Yang, Philip S. Yu

Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas.

Federated Learning

Network Embedding with Completely-imbalanced Labels

1 code implementation IEEE Transactions on Knowledge and Data Engineering 2020 Zheng Wang, Xiaojun Ye, Chaokun Wang, Jian Cui, Philip S. Yu

Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research.

Network Embedding

GCN for HIN via Implicit Utilization of Attention and Meta-paths

no code implementations6 Jul 2020 Di Jin, Zhizhi Yu, Dongxiao He, Carl Yang, Philip S. Yu, Jiawei Han

Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors.

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

no code implementations4 Jul 2020 Jianguo Chen, Kenli Li, Zhaolei Zhang, Keqin Li, Philip S. Yu

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

2 code implementations23 Jun 2020 Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu

To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.

Representation Learning

Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College

1 code implementation10 Jun 2020 Chen Li, Xutan Peng, Hao Peng, Jian-Xin Li, Lihong Wang, Philip S. Yu, Lifang He

Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.

Graph Attention Graph Generation

Robust Spammer Detection by Nash Reinforcement Learning

1 code implementation10 Jun 2020 Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie

We experiment on three large review datasets using various state-of-the-art spamming and detection strategies and show that the optimization algorithm can reliably find an equilibrial detector that can robustly and effectively prevent spammers with any mixed spamming strategies from attaining their practical goal.

Fraud Detection

User Memory Reasoning for Conversational Recommendation

no code implementations COLING 2020 Hu Xu, Seungwhan Moon, Honglei Liu, Pararth Shah, Bing Liu, Philip S. Yu

We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph, to allow for natural interactions and accurate recommendations.

Joint Training Capsule Network for Cold Start Recommendation

no code implementations23 May 2020 Ting-Ting Liang, Congying Xia, Yuyu Yin, Philip S. Yu

This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task.

Deep Learning for Community Detection: Progress, Challenges and Opportunities

1 code implementation17 May 2020 Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, Philip S. Yu

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.

Community Detection Graph Embedding

Commonsense Evidence Generation and Injection in Reading Comprehension

no code implementations11 May 2020 Ye Liu, Tao Yang, Zeyu You, Wei Fan, Philip S. Yu

Human tackle reading comprehension not only based on the given context itself but often rely on the commonsense beyond.

Language Modelling Reading Comprehension

Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

1 code implementation1 May 2020 Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng

In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and (3) for the relation inconsistency, we learn a relation attention weights associated with the sampled nodes.

Fraud Detection

DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis

1 code implementation Findings of the Association for Computational Linguistics 2020 Hu Xu, Bing Liu, Lei Shu, Philip S. Yu

This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding.

Aspect-Based Sentiment Analysis Language Modelling

Differentially Private Deep Learning with Smooth Sensitivity

no code implementations1 Mar 2020 Lichao Sun, Yingbo Zhou, Philip S. Yu, Caiming Xiong

Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.

Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping

1 code implementation11 Feb 2020 Shao-Heng Ko, Hsu-Chao Lai, Hong-Han Shuai, De-Nian Yang, Wang-Chien Lee, Philip S. Yu

Shopping in VR malls has been regarded as a paradigm shift for E-commerce, but most of the conventional VR shopping platforms are designed for a single user.

Data Structures and Algorithms

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

1 code implementation2 Feb 2020 Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu

In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.

Knowledge Graph Completion Knowledge Graph Embedding +1

Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations

1 code implementation18 Jan 2020 Huanrui Luo, Ning Yang, Philip S. Yu

Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item.

Deep Collaborative Embedding for information cascade prediction

no code implementations18 Jan 2020 Yuhui Zhao, Ning Yang, Tao Lin, Philip S. Yu

First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion.

BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network

1 code implementation14 Jan 2020 Zhiwei Liu, Mengting Wan, Stephen Guo, Kannan Achan, Philip S. Yu

By defining a basket entity to represent the basket intent, we can model this problem as a basket-item link prediction task in the User-Basket-Item~(UBI) graph.

Link Prediction

Leveraging Semi-Supervised Learning for Fairness using Neural Networks

no code implementations31 Dec 2019 Vahid Noroozi, Sara Bahaadini, Samira Sheikhi, Nooshin Mojab, Philip S. Yu

There has been a growing concern about the fairness of decision-making systems based on machine learning.

Decision Making Fairness

Deep Graph Similarity Learning: A Survey

no code implementations25 Dec 2019 Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke, Philip S. Yu

In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search.

Graph Similarity

VideoDG: Generalizing Temporal Relations in Videos to Novel Domains

1 code implementation8 Dec 2019 Zhiyu Yao, Yunbo Wang, Jianmin Wang, Philip S. Yu, Mingsheng Long

This paper introduces video domain generalization where most video classification networks degenerate due to the lack of exposure to the target domains of divergent distributions.

Action Recognition Data Augmentation +3

Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings

no code implementations6 Dec 2019 Shaika Chowdhury, Chenwei Zhang, Philip S. Yu, Yuan Luo

Distributed representations of medical concepts have been used to support downstream clinical tasks recently.

Decision Making

Generative Temporal Link Prediction via Self-tokenized Sequence Modeling

no code implementations26 Nov 2019 Yue Wang, Chenwei Zhang, Shen Wang, Philip S. Yu, Lu Bai, Lixin Cui, Guandong Xu

We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.

Link Prediction Tokenization

A Domain Adaptive Density Clustering Algorithm for Data with Varying Density Distribution

1 code implementation23 Nov 2019 Jianguo Chen, Philip S. Yu

However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribution (ED), and multiple domain-density maximums (MDDM), leading to the problems of sparse cluster loss and cluster fragmentation.

Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks

no code implementations8 Nov 2019 Jiahao Liu, Guixiang Ma, Fei Jiang, Chun-Ta Lu, Philip S. Yu, Ann B. Ragin

Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity.


A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution

1 code implementation4 Nov 2019 Hu Xu, Bing Liu, Lei Shu, Philip S. Yu

Aspect-based sentiment classification (ASC) is an important task in fine-grained sentiment analysis.~Deep supervised ASC approaches typically model this task as a pair-wise classification task that takes an aspect and a sentence containing the aspect and outputs the polarity of the aspect in that sentence.

General Classification Sentiment Analysis

JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation

1 code implementation18 Oct 2019 Zhiwei Liu, Lei Zheng, Jiawei Zhang, Jiayu Han, Philip S. Yu

JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant user representation with a domain adaptive user mapping module.

Recommendation Systems

Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction

no code implementations15 Oct 2019 Shaika Chowdhury, Chenwei Zhang, Philip S. Yu, Yuan Luo

Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients.

Mortality Prediction Semantic correspondence

Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

no code implementations14 Sep 2019 Chuan Shi, Xiaotian Han, Li Song, Xiao Wang, Senzhang Wang, Junping Du, Philip S. Yu

However, the characteristics of users and the properties of items may stem from different aspects, e. g., the brand-aspect and category-aspect of items.

Recommendation Systems

Temporal Network Embedding with Micro- and Macro-dynamics

1 code implementation10 Sep 2019 Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye

The micro-dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale.

Network Embedding

Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System

1 code implementation13 Aug 2019 Ye Liu, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu

To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards.

Question Answering Word Embeddings

Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking

no code implementations13 Aug 2019 Yue Wang, Yao Wan, Chenwei Zhang, Lixin Cui, Lu Bai, Philip S. Yu

During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously.

Decision Making Multi-agent Reinforcement Learning

Uncovering Download Fraud Activities in Mobile App Markets

no code implementations5 Jul 2019 Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu

To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.

Deep Learning for Spatio-Temporal Data Mining: A Survey

no code implementations11 Jun 2019 Senzhang Wang, Jiannong Cao, Philip S. Yu

Next we classify existing literatures based on the types of ST data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience.

Anomaly Detection Representation Learning

Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

no code implementations9 Jun 2019 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Lifang He, Bo Li, Lihong Wang, Philip S. Yu

In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.

Classification General Classification +2

Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, Philip S. Yu

In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pair-wise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model.

Event Detection

Private Deep Learning with Teacher Ensembles

no code implementations5 Jun 2019 Lichao Sun, Yingbo Zhou, Ji Wang, Jia Li, Richard Sochar, Philip S. Yu, Caiming Xiong

Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information.

Ensemble Learning Knowledge Distillation +1

Self-Activation Influence Maximization

no code implementations5 Jun 2019 Lichao Sun, Albert Chen, Philip S. Yu, Wei Chen

We incorporate self activation into influence propagation and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as seeds, and influence propagates from both selected seeds and self activated nodes.

Social and Information Networks

Missing Movie Synergistic Completion across Multiple Isomeric Online Movie Knowledge Libraries

no code implementations15 May 2019 Bowen Dong, Jiawei Zhang, Chenwei Zhang, Yang Yang, Philip S. Yu

Online knowledge libraries refer to the online data warehouses that systematically organize and categorize the knowledge-based information about different kinds of concepts and entities.

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

no code implementations12 Apr 2019 Jianguo Chen, Kenli Li, Qingying Deng, Keqin Li, Philip S. Yu

We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing.


BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis

1 code implementation NAACL 2019 Hu Xu, Bing Liu, Lei Shu, Philip S. Yu

Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC.

Aspect Extraction

Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce

no code implementations NAACL 2019 Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Xiuming Pan, Yu Gong, Philip S. Yu

To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process.

Mutual Clustering on Comparative Texts via Heterogeneous Information Networks

no code implementations9 Mar 2019 Jianping Cao, Senzhang Wang, Danyan Wen, Zhaohui Peng, Philip S. Yu, Fei-Yue Wang

HINT first models multi-sourced texts (e. g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts.

Text Clustering Transfer Learning

Multi-Hot Compact Network Embedding

no code implementations7 Mar 2019 Chaozhuo Li, Senzhang Wang, Philip S. Yu, Zhoujun Li

Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features.

Network Embedding

Spatiotemporal Pyramid Network for Video Action Recognition

no code implementations CVPR 2017 Yunbo Wang, Mingsheng Long, Jian-Min Wang, Philip S. Yu

From the technical perspective, we introduce the spatiotemporal compact bilinear operator into video analysis tasks.

Action Recognition

Fused Lasso for Feature Selection using Structural Information

no code implementations26 Feb 2019 Lu Bai, Lixin Cui, Yue Wang, Philip S. Yu, Edwin R. Hancock

To overcome these issues, we propose a new feature selection method using structural correlation between pairwise samples.

Feature Selection Time Series +1

Review Conversational Reading Comprehension

1 code implementation3 Feb 2019 Hu Xu, Bing Liu, Lei Shu, Philip S. Yu

Inspired by conversational reading comprehension (CRC), this paper studies a novel task of leveraging reviews as a source to build an agent that can answer multi-turn questions from potential consumers of online businesses.

Language Modelling Machine Reading Comprehension

DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

2 code implementations15 Jan 2019 Zhi-Hong Deng, Ling Huang, Chang-Dong Wang, Jian-Huang Lai, Philip S. Yu

To solve this problem, many methods have been studied, which can be generally categorized into two types, i. e., representation learning-based CF methods and matching function learning-based CF methods.

Recommendation Systems Representation Learning

A Comprehensive Survey on Graph Neural Networks

6 code implementations3 Jan 2019 Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu

In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.

Image Classification Natural Language Understanding +1

Entity Synonym Discovery via Multipiece Bilateral Context Matching

1 code implementation31 Dec 2018 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization.

Entity Disambiguation

Adversarial Attack and Defense on Graph Data: A Survey

1 code implementation26 Dec 2018 Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Lifang He, Bo Li

Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph.

Adversarial Attack Image Classification +1

Joint Slot Filling and Intent Detection via Capsule Neural Networks

3 code implementations ACL 2019 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.

Intent Detection Natural Language Understanding +1

Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics

2 code implementations CVPR 2019 Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long, Jian-Min Wang, Philip S. Yu

Natural spatiotemporal processes can be highly non-stationary in many ways, e. g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting.

Time Series Time Series Forecasting +1

Improving Automatic Source Code Summarization via Deep Reinforcement Learning

2 code implementations17 Nov 2018 Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, Philip S. Yu

To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization, b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given.

Code Summarization Source Code Summarization

Private Model Compression via Knowledge Distillation

no code implementations13 Nov 2018 Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, Philip S. Yu

To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.

Knowledge Distillation Model Compression

Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training

no code implementations12 Nov 2018 Yao Wan, Wenqiang Yan, Jianwei Gao, Zhou Zhao, Jian Wu, Philip S. Yu

Dialogue Act (DA) classification is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention.

Classification Dialogue Act Classification +3

Generative Dual Adversarial Network for Generalized Zero-shot Learning

1 code implementation CVPR 2019 He Huang, Changhu Wang, Philip S. Yu, Chang-Dong Wang

Most previous models try to learn a fixed one-directional mapping between visual and semantic space, while some recently proposed generative methods try to generate image features for unseen classes so that the zero-shot learning problem becomes a traditional fully-supervised classification problem.

Generalized Zero-Shot Learning Metric Learning

Graph Convolutional Neural Networks via Motif-based Attention

no code implementations11 Nov 2018 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Yuanxing Ning, Philip S. Yu

Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information.

General Classification Graph Classification

Product Title Refinement via Multi-Modal Generative Adversarial Learning

no code implementations11 Nov 2018 Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Ye Liu, Xiuming Pan, Yu Gong, Philip S. Yu

Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products.

Semi-supervised Deep Representation Learning for Multi-View Problems

no code implementations11 Nov 2018 Vahid Noroozi, Sara Bahaadini, Lei Zheng, Sihong Xie, Weixiang Shao, Philip S. Yu

While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied.

Dimensionality Reduction Learning Representation Of Multi-View Data

Securing Behavior-based Opinion Spam Detection

no code implementations9 Nov 2018 Shuaijun Ge, Guixiang Ma, Sihong Xie, Philip S. Yu

In terms of security, DETER is versatile enough to be vaccinated against diverse and unexpected evasions, is agnostic about evasion strategy and can be released without privacy concern.

Data-driven Blockbuster Planning on Online Movie Knowledge Library

no code implementations24 Oct 2018 Ye Liu, Jiawei Zhang, Chenwei Zhang, Philip S. Yu

After a thorough investigation of an online movie knowledge library, a novel movie planning framework "Blockbuster Planning with Maximized Movie Configuration Acquaintance" (BigMovie) is introduced in this paper.

A Self-Organizing Tensor Architecture for Multi-View Clustering

no code implementations18 Oct 2018 Lifang He, Chun-Ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, Fei Wang

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.

A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks

no code implementations17 Oct 2018 Jianguo Chen, Kenli Li, Kashif Bilal, Xu Zhou, Keqin Li, Philip S. Yu

In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments.

Distributed Computing

A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments

no code implementations17 Oct 2018 Jianguo Chen, Kenli Li, Huigui Rong, Kashif Bilal, Keqin Li, Philip S. Yu

In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment.

Distributed Computing Time Series +1

Modeling relation paths for knowledge base completion via joint adversarial training

1 code implementation14 Oct 2018 Chen Li, Xutan Peng, Shanghang Zhang, Hao Peng, Philip S. Yu, Min He, Linfeng Du, Lihong Wang

By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i. e. relation classifier and source discriminator), to capture shared/similar information between them.

Knowledge Base Completion

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

no code implementations11 Sep 2018 Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu

Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.

Network Embedding Tensor Decomposition

Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction

no code implementations11 Sep 2018 Jian-Guo Zhang, Ji Wang, Lifang He, Zhao Li, Philip S. Yu

Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model.

Anomaly Detection

Deep Learning Towards Mobile Applications

no code implementations10 Sep 2018 Ji Wang, Bokai Cao, Philip S. Yu, Lichao Sun, Weidong Bao, Xiaomin Zhu

In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning.

Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud

no code implementations10 Sep 2018 Ji Wang, Jian-Guo Zhang, Weidong Bao, Xiaomin Zhu, Bokai Cao, Philip S. Yu

To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers.

Deep Priority Hashing

1 code implementation4 Sep 2018 Zhangjie Cao, Ziping Sun, Mingsheng Long, Jian-Min Wang, Philip S. Yu

Deep hashing enables image retrieval by end-to-end learning of deep representations and hash codes from training data with pairwise similarity information.

Image Retrieval Quantization

Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data

no code implementations2 Sep 2018 Xi Zhang, Yixuan Li, Senzhang Wang, Binxing Fang, Philip S. Yu

In this work, we study how to explore multiple data sources to improve the performance of the stock prediction.

Stock Prediction

Spectral Collaborative Filtering

1 code implementation30 Aug 2018 Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, Philip S. Yu

Benefiting from the rich information of connectivity existing in the \textit{spectral domain}, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the \textit{cold-start} problem for CF.

Recommendation Systems

dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction

1 code implementation29 Aug 2018 He Huang, Bokai Cao, Philip S. Yu, Chang-Dong Wang, Alex D. Leow

Mood disorders are common and associated with significant morbidity and mortality.

Human-Computer Interaction Computers and Society

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

no code implementations19 Jun 2018 Ye Liu, Lifang He, Bokai Cao, Philip S. Yu, Ann B. Ragin, Alex D. Leow

Network analysis of human brain connectivity is critically important for understanding brain function and disease states.

Graph Embedding

TI-CNN: Convolutional Neural Networks for Fake News Detection

2 code implementations3 Jun 2018 Yang Yang, Lei Zheng, Jiawei Zhang, Qingcai Cui, Zhoujun Li, Philip S. Yu

By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously.

Fact Checking Fake News Detection

r-Instance Learning for Missing People Tweets Identification

no code implementations28 May 2018 Yang Yang, Haoyan Liu, Xia Hu, Jiawei Zhang, Xiao-Ming Zhang, Zhoujun Li, Philip S. Yu

The number of missing people (i. e., people who get lost) greatly increases in recent years.

Lifelong Domain Word Embedding via Meta-Learning

1 code implementation25 May 2018 Hu Xu, Bing Liu, Lei Shu, Philip S. Yu

Learning high-quality domain word embeddings is important for achieving good performance in many NLP tasks.

Meta-Learning Word Embeddings

FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network

2 code implementations22 May 2018 Jiawei Zhang, Bowen Dong, Philip S. Yu

This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance.

Fake News Detection

Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction

2 code implementations ACL 2018 Hu Xu, Bing Liu, Lei Shu, Philip S. Yu

Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings.

Aspect Extraction

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

no code implementations23 Mar 2018 Bokai Cao, Lei Zheng, Chenwei Zhang, Philip S. Yu, Andrea Piscitello, John Zulueta, Olu Ajilore, Kelly Ryan, Alex D. Leow

The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives.

An Introduction to Image Synthesis with Generative Adversarial Nets

no code implementations12 Mar 2018 He Huang, Philip S. Yu, Changhu Wang

There has been a drastic growth of research in Generative Adversarial Nets (GANs) in the past few years.

Image-to-Image Translation

Multi-Round Influence Maximization (Extended Version)

1 code implementation12 Feb 2018 Lichao Sun, Weiran Huang, Philip S. Yu, Wei Chen

In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where influence propagates in multiple rounds independently from possibly different seed sets, and the goal is to select seeds for each round to maximize the expected number of nodes that are activated in at least one round.

Social and Information Networks

Multi-Task Pharmacovigilance Mining from Social Media Posts

no code implementations19 Jan 2018 Shaika Chowdhury, Chenwei Zhang, Philip S. Yu

Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported.

Lifelong Word Embedding via Meta-Learning

no code implementations ICLR 2018 Hu Xu, Bing Liu, Lei Shu, Philip S. Yu

We observe that domains are not isolated and a small domain corpus can leverage the learned knowledge from many past domains to augment that corpus in order to generate high-quality embeddings.

Meta-Learning Word Embeddings

Generative Discovery of Relational Medical Entity Pairs

no code implementations ICLR 2018 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies.

Error-Robust Multi-View Clustering

no code implementations1 Jan 2018 Mehrnaz Najafi, Lifang He, Philip S. Yu

Various types of errors behave differently and inconsistently in each view.

Stratified Transfer Learning for Cross-domain Activity Recognition

no code implementations25 Dec 2017 Jindong Wang, Yiqiang Chen, Lisha Hu, Xiaohui Peng, Philip S. Yu

The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition.

Cross-Domain Activity Recognition General Classification +1

Product Function Need Recognition via Semi-supervised Attention Network

no code implementations6 Dec 2017 Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu

Functionality is of utmost importance to customers when they purchase products.

Dual Attention Network for Product Compatibility and Function Satisfiability Analysis

no code implementations6 Dec 2017 Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu

Product compatibility and their functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products.

PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs

no code implementations NeurIPS 2017 Yunbo Wang, Mingsheng Long, Jian-Min Wang, Zhifeng Gao, Philip S. Yu

The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously.

Video Prediction

Heterogeneous Information Network Embedding for Recommendation

1 code implementation29 Nov 2017 Chuan Shi, Binbin Hu, Wayne Xin Zhao, Philip S. Yu

In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec.

Social and Information Networks