Search Results for author: Tong Chen

Found 112 papers, 38 papers with code

A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems

1 code implementation25 Jun 2024 Hung Vinh Tran, Tong Chen, Quoc Viet Hung Nguyen, Zi Huang, Lizhen Cui, Hongzhi Yin

State-of-the-art RSs primarily depend on categorical features, which ecoded by embedding vectors, resulting in excessively large embedding tables.

Benchmarking Collaborative Filtering +2

EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy

1 code implementation19 Jun 2024 Long Bai, Tong Chen, Qiaozhi Tan, Wan Jun Nah, Yanheng Li, ZhiCheng He, Sishen Yuan, Zhen Chen, Jinlin Wu, Mobarakol Islam, Zhen Li, Hongbin Liu, Hongliang Ren

While researchers have discussed the challenges of low-light enhancement in WCE, the issue of correcting for different exposure levels remains underexplored.

Image Enhancement Navigate +1

RobGC: Towards Robust Graph Condensation

no code implementations19 Jun 2024 Xinyi Gao, Hongzhi Yin, Tong Chen, Guanhua Ye, Wentao Zhang, Bin Cui

Graph neural networks (GNNs) have attracted widespread attention for their impressive capability of graph representation learning.

Denoising Graph Representation Learning

Hate Speech Detection with Generalizable Target-aware Fairness

1 code implementation28 May 2024 Tong Chen, Danny Wang, Xurong Liang, Marten Risius, Gianluca Demartini, Hongzhi Yin

To counter the side effect brought by the proliferation of social media platforms, hate speech detection (HSD) plays a vital role in halting the dissemination of toxic online posts at an early stage.

Fairness Hate Speech Detection

Graph Condensation for Open-World Graph Learning

no code implementations27 May 2024 Xinyi Gao, Tong Chen, Wentao Zhang, Yayong Li, Xiangguo Sun, Hongzhi Yin

Consequently, due to the limited generalization capacity of condensed graphs, applications that employ GC for efficient GNN training end up with sub-optimal GNNs when confronted with evolving graph structures and distributions in dynamic real-world situations.

Graph Learning

Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations

no code implementations22 May 2024 Jing Long, Guanhua Ye, Tong Chen, Yang Wang, Meng Wang, Hongzhi Yin

The rapid expansion of Location-Based Social Networks (LBSNs) has highlighted the importance of effective next Point-of-Interest (POI) recommendations, which leverage historical check-in data to predict users' next POIs to visit.

Federated Learning

Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

no code implementations22 May 2024 Xinyi Gao, Tong Chen, Wentao Zhang, Junliang Yu, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin

The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements.

Graph Neural Network

Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness

1 code implementation4 May 2024 Xinran Zhao, Tong Chen, Sihao Chen, Hongming Zhang, Tongshuang Wu

In this work, we study whether retrievers can recognize and respond to different perspectives of the queries -- beyond finding relevant documents for a claim, can retrievers distinguish supporting vs. opposing documents?

Information Retrieval Retrieval

Poisoning Decentralized Collaborative Recommender System and Its Countermeasures

no code implementations1 Apr 2024 Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng, Yuhui Shi, Hongzhi Yin

Knowledge sharing also opens a backdoor for model poisoning attacks, where adversaries disguise themselves as benign clients and disseminate polluted knowledge to achieve malicious goals like promoting an item's exposure rate.

Model Poisoning Recommendation Systems

Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency

2 code implementations14 Mar 2024 Hallgrimur Thorsteinsson, Valdemar J Henriksen, Tong Chen, Raghavendra Selvan

We present experiments on two benchmark datasets showing that adversarial fine-tuning of compressed models can achieve robustness performance comparable to adversarially trained models, while also improving computational efficiency.

Adversarial Robustness Model Compression +2

TinyLIC-High efficiency lossy image compression method

no code implementations17 Feb 2024 Gaocheng Ma, Yinfeng Chai, Tianhao Jiang, Ming Lu, Tong Chen

Image compression has been the subject of extensive research for several decades, resulting in the development of well-known standards such as JPEG, JPEG2000, and H. 264/AVC.

Image Compression

Is Adversarial Training with Compressed Datasets Effective?

1 code implementation8 Feb 2024 Tong Chen, Raghavendra Selvan

This synthetic dataset retains the essential information of the original dataset, enabling models trained on it to achieve performance levels comparable to those trained on the full dataset.

Adversarial Robustness Dataset Condensation

Challenging Low Homophily in Social Recommendation

no code implementations26 Jan 2024 Wei Jiang, Xinyi Gao, Guandong Xu, Tong Chen, Hongzhi Yin

To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models.

Contrastive Learning

Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation

no code implementations24 Jan 2024 Ruiqi Zheng, Liang Qu, Tong Chen, Lizhen Cui, Yuhui Shi, Hongzhi Yin

Collaborative Learning (CL) emerges to promote model sharing among users, where reference data is an intermediary that allows users to exchange their soft decisions without directly sharing their private data or parameters, ensuring privacy and benefiting from collaboration.

Graph Condensation: A Survey

no code implementations22 Jan 2024 Xinyi Gao, Junliang Yu, Wei Jiang, Tong Chen, Wentao Zhang, Hongzhi Yin

The burgeoning volume of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs).

Fairness Graph Generation

On-Device Recommender Systems: A Comprehensive Survey

no code implementations21 Jan 2024 Hongzhi Yin, Liang Qu, Tong Chen, Wei Yuan, Ruiqi Zheng, Jing Long, Xin Xia, Yuhui Shi, Chengqi Zhang

Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training.

Recommendation Systems

Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

no code implementations25 Dec 2023 Lijian Chen, Wei Yuan, Tong Chen, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin

Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences.

Recommendation Systems

On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm

no code implementations18 Dec 2023 Hongzhi Yin, Tong Chen, Liang Qu, Bin Cui

Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry.

Recommendation Systems

Dense X Retrieval: What Retrieval Granularity Should We Use?

2 code implementations11 Dec 2023 Tong Chen, Hongwei Wang, Sihao Chen, Wenhao Yu, Kaixin Ma, Xinran Zhao, Hongming Zhang, Dong Yu

We discover that the retrieval unit choice significantly impacts the performance of both retrieval and downstream tasks.

Retrieval Sentence +1

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

1 code implementation19 Nov 2023 Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution.

Anomaly Detection Contrastive Learning +3

Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations

1 code implementation7 Nov 2023 Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu

We introduce sub-sentence encoder, a contrastively-learned contextual embedding model for fine-grained semantic representation of text.

Contrastive Learning Semantic Similarity +3

Budgeted Embedding Table For Recommender Systems

no code implementations23 Oct 2023 Yunke Qu, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

Experiments have shown state-of-the-art performance on two real-world datasets when BET is paired with three popular recommender models under different memory budgets.

Recommendation Systems Representation Learning

To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data

1 code implementation15 Sep 2023 Hechuan Wen, Tong Chen, Li Kheng Chai, Shazia Sadiq, Kai Zheng, Hongzhi Yin

Due to the imbalanced nature of networked observational data, the causal effect predictions for some individuals can severely violate the positivity/overlap assumption, rendering unreliable estimations.

Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation

1 code implementation7 Sep 2023 Xurong Liang, Tong Chen, Quoc Viet Hung Nguyen, JianXin Li, Hongzhi Yin

In addition, we innovatively design a regularized pruning mechanism in CERP, such that the two sparsified meta-embedding tables are encouraged to encode information that is mutually complementary.

Recommendation Systems

Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation

no code implementations25 Aug 2023 Guanhua Ye, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers have to put unlearning functionality into their consideration.

Machine Unlearning

Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

no code implementations15 Aug 2023 Yi Liu, Hongrui Xuan, Bohan Li, Meng Wang, Tong Chen, Hongzhi Yin

However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement.

Collaborative Filtering Knowledge-Aware Recommendation +2

Graph Condensation for Inductive Node Representation Learning

no code implementations29 Jul 2023 Xinyi Gao, Tong Chen, Yilong Zang, Wentao Zhang, Quoc Viet Hung Nguyen, Kai Zheng, Hongzhi Yin

To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning.

Representation Learning

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

1 code implementation5 Jul 2023 Long Bai, Tong Chen, Yanan Wu, An Wang, Mobarakol Islam, Hongliang Ren

Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process.

Denoising Low-Light Image Enhancement

Variational Counterfactual Prediction under Runtime Domain Corruption

no code implementations23 Jun 2023 Hechuan Wen, Tong Chen, Li Kheng Chai, Shazia Sadiq, Junbin Gao, Hongzhi Yin

We term the co-occurrence of domain shift and inaccessible variables runtime domain corruption, which seriously impairs the generalizability of a trained counterfactual predictor.

counterfactual Domain Adaptation +1

Personalized Elastic Embedding Learning for On-Device Recommendation

no code implementations18 Jun 2023 Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng, Yuhui Shi, Hongzhi Yin

Given a memory budget, PEEL efficiently generates PEEs by selecting embedding blocks with the largest weights, making it adaptable to dynamic memory budgets on devices.


Do as I can, not as I get

no code implementations17 Jun 2023 Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, Lei Zhao

This paper proposes a model called TMR to mine valuable information from simulated data environments.

Knowledge Graphs Multi-modal Knowledge Graph +1

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

1 code implementation11 May 2023 Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, Hongzhi Yin

Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.

Machine Unlearning Response Generation

Explicit Knowledge Graph Reasoning for Conversational Recommendation

no code implementations1 May 2023 Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen, Lizhen Cui, Zi Huang, Hongzhi Yin

Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions.

Attribute Recommendation Systems

Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling

no code implementations24 Apr 2023 Xuhui Ren, Wei Yuan, Tong Chen, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin

Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences.

Knowledge Graphs Language Modelling +2

Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation

no code implementations8 Apr 2023 Jing Long, Tong Chen, Nguyen Quoc Viet Hung, Guandong Xu, Kai Zheng, Hongzhi Yin

In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e. g., dimension \& number of hidden layers).

Knowledge Distillation Privacy Preserving

DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning

no code implementations8 Apr 2023 Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, Lei Zhao

Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability.

Knowledge Graphs Missing Elements +3

Continuous Input Embedding Size Search For Recommender Systems

no code implementations7 Apr 2023 Yunke Qu, Tong Chen, Xiangyu Zhao, Lizhen Cui, Kai Zheng, Hongzhi Yin

Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance.

Recommendation Systems Reinforcement Learning (RL)

TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT

no code implementations7 Mar 2023 Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices.

Anomaly Detection Time Series +1

Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks

no code implementations27 Feb 2023 Xinyi Gao, Wentao Zhang, Tong Chen, Junliang Yu, Hung Quoc Viet Nguyen, Hongzhi Yin

To tackle the imbalance of minority classes and supplement their inadequate semantics, we present the first method for the semantic imbalance problem in imbalanced HINs named Semantic-aware Node Synthesis (SNS).

Representation Learning

Self-supervised Graph-based Point-of-interest Recommendation

no code implementations22 Oct 2022 Yang Li, Tong Chen, Peng-Fei Zhang, Zi Huang, Hongzhi Yin

In order to counteract the scarcity and incompleteness of POI check-ins, we propose a novel self-supervised learning paradigm in \ssgrec, where the trajectory representations are contrastively learned from two augmented views on geolocations and temporal transitions.

Self-Supervised Learning

XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation

1 code implementation6 Sep 2022 Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, Hongzhi Yin

Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance.

Contrastive Learning

Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution

no code implementations1 Jul 2022 Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Quoc Viet Hung Nguyen, Xiaofang Zhou, Lei Chen

Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information.

Dynamic graph embedding Graph Mining

Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation

1 code implementation16 Jun 2022 Lei Guo, Jinyu Zhang, Li Tang, Tong Chen, Lei Zhu, Hongzhi Yin

Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains.

Graph Neural Network Representation Learning +2

Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation

1 code implementation16 Jun 2022 Lei Guo, Jinyu Zhang, Tong Chen, Xinhua Wang, Hongzhi Yin

Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation.

Graph Neural Network Hierarchical Reinforcement Learning +3

Spatial-Temporal Meta-path Guided Explainable Crime Prediction

no code implementations4 May 2022 Yuting Sun, Tong Chen, Hongzhi Yin

Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities.

BIG-bench Machine Learning Crime Prediction

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

1 code implementation6 Apr 2022 Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, Meng Wang

Such user and group preferences are commonly represented as points in the vector space (i. e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs.

Decision Making

Decentralized Collaborative Learning Framework for Next POI Recommendation

no code implementations30 Mar 2022 Jing Long, Tong Chen, Nguyen Quoc Viet Hung, Hongzhi Yin

On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner.

Privacy Preserving

Self-Supervised Learning for Recommender Systems: A Survey

1 code implementation29 Mar 2022 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, Zi Huang

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data.

Recommendation Systems Self-Supervised Learning

Unified Question Generation with Continual Lifelong Learning

no code implementations24 Jan 2022 Wei Yuan, Hongzhi Yin, Tieke He, Tong Chen, Qiufeng Wang, Lizhen Cui

To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats.

Question Answering Question Generation +1

Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent

no code implementations17 Dec 2021 Guanhua Ye, Hongzhi Yin, Tong Chen, Miao Xu, Quoc Viet Hung Nguyen, Jiangning Song

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.

Benchmarking Fairness +1

Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation

1 code implementation16 Dec 2021 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, Quoc Viet Hung Nguyen

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue.

Contrastive Learning Recommendation Systems

SurvODE: Extrapolating Gene Expression Distribution for Early Cancer Identification

no code implementations30 Nov 2021 Tong Chen, Sheng Wang

With the increasingly available large-scale cancer genomics datasets, machine learning approaches have played an important role in revealing novel insights into cancer development.

Irregular Time Series Time Series +1

PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion

no code implementations21 Oct 2021 Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Quoc Viet Hung Nguyen, Lizhen Cui

Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.

Federated Learning Model Poisoning +1

Localization with Sampling-Argmax

1 code implementation NeurIPS 2021 Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-Lu Li, Cewu Lu

In this work, we propose sampling-argmax, a differentiable training method that imposes implicit constraints to the shape of the probability map by minimizing the expectation of the localization error.

3D Human Pose Estimation

Lightweight Self-Attentive Sequential Recommendation

no code implementations25 Aug 2021 Yang Li, Tong Chen, Peng-Fei Zhang, Hongzhi Yin

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks.

Sequential Recommendation

Attribute-aware Explainable Complementary Clothing Recommendation

no code implementations4 Jul 2021 Yang Li, Tong Chen, Zi Huang

As a result, this creates a severe bottleneck when we are trying to advance the recommendation accuracy and generating fine-grained explanations since the explicit attributes have only loose connections to the actual recommendation process.

Attribute Recommendation Systems

Exploiting Positional Information for Session-based Recommendation

no code implementations2 Jul 2021 Ruihong Qiu, Zi Huang, Tong Chen, Hongzhi Yin

According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session.

Graph Neural Network Session-Based Recommendations

Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

no code implementations30 Jun 2021 Yang Li, Tong Chen, Yadan Luo, Hongzhi Yin, Zi Huang

Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation.

Multi-Task Learning

Learning Elastic Embeddings for Customizing On-Device Recommenders

no code implementations4 Jun 2021 Tong Chen, Hongzhi Yin, Yujia Zheng, Zi Huang, Yang Wang, Meng Wang

The core idea is to compose elastic embeddings for each item, where an elastic embedding is the concatenation of a set of embedding blocks that are carefully chosen by an automated search function.

Recommendation Systems

Learning to Ask Appropriate Questions in Conversational Recommendation

no code implementations11 May 2021 Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, Kai Zheng

Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations.

Question Generation Question-Generation +1

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

no code implementations7 May 2021 Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, Hongzhi Yin

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.

Sequential Recommendation Transfer Learning

Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling

no code implementations5 Apr 2021 Tong Chen, Hongzhi Yin, Xiangliang Zhang, Zi Huang, Yang Wang, Meng Wang

As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.

Feature Engineering

Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

no code implementations4 Apr 2021 Tong Chen, Hongzhi Yin, Jie Ren, Zi Huang, Xiangliang Zhang, Hao Wang

In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.

Graph Representation Learning Transductive Learning

Fast-adapting and Privacy-preserving Federated Recommender System

no code implementations2 Apr 2021 Qinyong Wang, Hongzhi Yin, Tong Chen, Junliang Yu, Alexander Zhou, Xiangliang Zhang

In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.

Federated Learning Meta-Learning +2

Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation

no code implementations24 Mar 2021 Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, Kai Zheng

However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces.

Representation Learning

Graph Embedding for Recommendation against Attribute Inference Attacks

no code implementations29 Jan 2021 Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, Xiangliang Zhang

Specifically, in GERAI, we bind the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks.

Attribute Graph Embedding +2

Knowledge Graph Completion with Text-aided Regularization

no code implementations22 Jan 2021 Tong Chen, Sirou Zhu, Yiming Wen, Zhaomin Zheng

Knowledge Graph Completion is a task of expanding the knowledge graph/base through estimating possible entities, or proper nouns, that can be connected using a set of predefined relations, or verb/predicates describing interconnections of two things.

Knowledge Graph Completion

A Sublevel Moment-SOS Hierarchy for Polynomial Optimization

1 code implementation13 Jan 2021 Tong Chen, Jean-Bernard Lasserre, Victor Magron, Edouard Pauwels

We introduce a sublevel Moment-SOS hierarchy where each SDP relaxation can be viewed as an intermediate (or interpolation) between the d-th and (d+1)-th order SDP relaxations of the Moment-SOS hierarchy (dense or sparse version).

Combinatorial Optimization Optimization and Control

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

no code implementations8 Jan 2021 Guanhua Ye, Hongzhi Yin, Tong Chen, Hongxu Chen, Lizhen Cui, Xiangliang Zhang

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings.

Sleep apnea detection

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph

no code implementations4 Jan 2021 Yuandong Wang, Hongzhi Yin, Tong Chen, Chunyang Liu, Ben Wang, Tianyu Wo, Jie Xu

Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i. e., weights) of passenger demands between two connected regions.

Graph Attention Representation Learning +1

Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning

no code implementations1 Dec 2020 Ming Lu, Tong Chen, Dandan Ding, Fengqing Zhu, Zhan Ma

Inspired by the facts that retinal cells actually segregate the visual scene into different attributes (e. g., spatial details, temporal motion) for respective neuronal processing, we propose to first decompose the input video into respective spatial texture frames (STF) at its native spatial resolution that preserve the rich spatial details, and the other temporal motion frames (TMF) at a lower spatial resolution that retain the motion smoothness; then compress them together using any popular video coder; and finally synthesize decoded STFs and TMFs for high-fidelity video reconstruction at the same resolution as its native input.

Motion Compensation Super-Resolution +2

Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining

1 code implementation19 Sep 2020 Min Peng, Chongyang Wang, Yuan Gao, Tao Bi, Tong Chen, Yu Shi, Xiang-Dong Zhou

As a spontaneous expression of emotion on face, micro-expression reveals the underlying emotion that cannot be controlled by human.

Compiling ONNX Neural Network Models Using MLIR

1 code implementation19 Aug 2020 Tian Jin, Gheorghe-Teodor Bercea, Tung D. Le, Tong Chen, Gong Su, Haruki Imai, Yasushi Negishi, Anh Leu, Kevin O'Brien, Kiyokuni Kawachiya, Alexandre E. Eichenberger

Deep neural network models are becoming increasingly popular and have been used in various tasks such as computer vision, speech recognition, and natural language processing.

speech-recognition Speech Recognition

GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

1 code implementation6 Jul 2020 Ruihong Qiu, Hongzhi Yin, Zi Huang, Tong Chen

On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user.

Attribute Graph Neural Network +1

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

no code implementations2 Jun 2020 Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Tong Chen, Bogdan Gabrys, Katarzyna Musial

Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks.

Anchor link prediction Model Selection

Optimal multi-wave sampling for regression modelling in two-phase designs

no code implementations28 May 2020 Tong Chen, Thomas Lumley

We show that a two-wave sampling with reasonable informative priors will end up with higher precision for the parameter of interest and be close to the underlying optimal design.


GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection

1 code implementation20 May 2020 Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui

Therefore, it is of great practical significance to construct a robust recommender system that is able to generate stable recommendations even in the presence of shilling attacks.

Recommendation Systems Representation Learning

Try This Instead: Personalized and Interpretable Substitute Recommendation

no code implementations19 May 2020 Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, Meng Wang

Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i. e., personalization) and item-item relationships (i. e., substitution) for recommendation.

Attribute Collaborative Filtering +1

Semialgebraic Optimization for Lipschitz Constants of ReLU Networks

2 code implementations NeurIPS 2020 Tong Chen, Jean-Bernard Lasserre, Victor Magron, Edouard Pauwels

The Lipschitz constant of a network plays an important role in many applications of deep learning, such as robustness certification and Wasserstein Generative Adversarial Network.

Adversarial Robustness

Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

no code implementations13 Dec 2019 Haojie Liu, Han Shen, Lichao Huang, Ming Lu, Tong Chen, Zhan Ma

Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency.

Optical Flow Estimation Video Compression

Sequence-Aware Factorization Machines for Temporal Predictive Analytics

no code implementations7 Nov 2019 Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen, Wen-Chih Peng, Xue Li, Xiaofang Zhou

As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.

Recommendation Systems

Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling

1 code implementation11 Oct 2019 Tong Chen, Haojie Liu, Zhan Ma, Qiu Shen, Xun Cao, Yao Wang

This paper proposes a novel Non-Local Attention optmization and Improved Context modeling-based image compression (NLAIC) algorithm, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure.

Image Compression MS-SSIM +1

Learned Point Cloud Geometry Compression

2 code implementations26 Sep 2019 Jianqiang Wang, Hao Zhu, Zhan Ma, Tong Chen, Haojie Liu, Qiu Shen

This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a. k. a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE).

Surface Reconstruction

Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph

no code implementations25 Sep 2019 Woojeong Jin, He Jiang, Meng Qu, Tong Chen, Changlin Zhang, Pedro Szekely, Xiang Ren

We present Recurrent Event Network (RE-Net), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e. g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events.

Link Prediction Temporal Sequences

Collaborative Policy Learning for Open Knowledge Graph Reasoning

2 code implementations IJCNLP 2019 Cong Fu, Tong Chen, Meng Qu, Woojeong Jin, Xiang Ren

We propose a novel reinforcement learning framework to train two collaborative agents jointly, i. e., a multi-hop graph reasoner and a fact extractor.

Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack

no code implementations9 May 2019 Di Zhao, Jiqiang Liu, Jialin Wang, Wenjia Niu, Endong Tong, Tong Chen, Gang Li

"Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain.

Attribute Clustering

Non-local Attention Optimized Deep Image Compression

no code implementations22 Apr 2019 Haojie Liu, Tong Chen, Peiyao Guo, Qiu Shen, Xun Cao, Yao Wang, Zhan Ma

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure.

Image Compression MS-SSIM +1

Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization

no code implementations8 Apr 2019 Chao Huang, Haojie Liu, Tong Chen, Qiu Shen, Zhan Ma

We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate.

Generative Adversarial Network Image Compression

A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition

1 code implementation7 Apr 2019 Min Peng, Chongyang Wang, Tao Bi, Tong Chen, Xiangdong Zhou, Yu Shi

As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame.

Micro Expression Recognition Micro-Expression Recognition

Using Structured Input and Modularity for Improved Learning

no code implementations29 Mar 2019 Zehra Sura, Tong Chen, Hyojin Sung

Our method for utilizing the known structure of input data includes: (1) pre-processing the input data to expose relevant structures, and (2) constructing neural networks by incorporating the structure of the input data as an integral part of the network design.

Gated Context Model with Embedded Priors for Deep Image Compression

no code implementations27 Feb 2019 Haojie Liu, Tong Chen, Peiyao Guo, Qiu Shen, Zhan Ma

Besides, a field study on perceptual quality is also given via a dedicated subjective assessment, to compare the efficiency of our proposed methods and other conventional image compression methods.

Decoder Image Compression +3

Micro-Attention for Micro-Expression recognition

1 code implementation6 Nov 2018 Chongyang Wang, Min Peng, Tao Bi, Tong Chen

The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior.

Micro Expression Recognition Micro-Expression Recognition +1

Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding

no code implementations18 Jul 2018 Tong Chen, Wenjia Niu, Yingxiao Xiang, Xiaoxuan Bai, Jiqiang Liu, Zhen Han, Gang Li

In addition, we propose Gradient Band-based Adversarial Training, which trained with a single randomly choose dominant adversarial example without taking any modification, to realize the "1:N" attack immunity for generalized dominant adversarial examples.

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

1 code implementation13 Jun 2018 Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, Fei Wu

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.

Decoder Traffic Prediction

Deep Image Compression via End-to-End Learning

1 code implementation5 Jun 2018 Haojie Liu, Tong Chen, Qiu Shen, Tao Yue, Zhan Ma

We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate.

Image Compression MS-SSIM +3

When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity

no code implementations14 Oct 2017 Tong Chen, Lin Wu, Yang Wang, Jun Zhang, Hongxu Chen, Xue Li

Inspired by point process in modeling temporal point process, in this paper we present a deep prediction method based on two recurrent neural networks (RNNs) to jointly model each user's continuous browsing history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity.

Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

no code implementations20 Apr 2017 Tong Chen, Lin Wu, Xue Li, Jun Zhang, Hongzhi Yin, Yang Wang

The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.

Deep Attention

A single scale retinex based method for palm vein extraction

no code implementations26 May 2016 Chongyang Wang, Ming Peng, Lingfeng Xu, Tong Chen

Palm vein recognition is a novel biometric identification technology.

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