Search Results for author: Hui Xiong

Found 121 papers, 53 papers with code

DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load

1 code implementation21 Feb 2024 Siyang Li, Hui Xiong, Yize Chen

Accordingly, we devise a novel Diffusion model termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can explicitly approximate the predictive load distribution conditioned on historical data and related covariates.

Probabilistic Time Series Forecasting

Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation

no code implementations5 Feb 2024 Shuyao Wang, Yongduo Sui, Jiancan Wu, Zhi Zheng, Hui Xiong

In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment.

Model Compression Recommendation Systems +1

Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models

1 code implementation30 Jan 2024 Weijia Zhang, Jindong Han, Zhao Xu, Hang Ni, Hao liu, Hui Xiong

Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments.

GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling

no code implementations29 Jan 2024 Wei Ju, Yiyang Gu, Zhengyang Mao, Ziyue Qiao, Yifang Qin, Xiao Luo, Hui Xiong, Ming Zhang

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.

Adversarial Robustness Contrastive Learning +3

BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction

1 code implementation25 Jan 2024 Jiangmeng Li, Fei Song, Yifan Jin, Wenwen Qiang, Changwen Zheng, Fuchun Sun, Hui Xiong

From the perspective of distribution analyses, we disclose that the intrinsic issues behind the phenomenon are the over-multitudinous conceptual knowledge contained in PLMs and the abridged knowledge for target downstream domains, which jointly result in that PLMs mis-locate the knowledge distributions corresponding to the target domains in the universal knowledge embedding space.

Domain Adaptation

The Bigger the Better? Rethinking the Effective Model Scale in Long-term Time Series Forecasting

no code implementations22 Jan 2024 Jinliang Deng, Xuan Song, Ivor W. Tsang, Hui Xiong

Through this work, we advocate a paradigm shift in LTSF, emphasizing the importance to tailor the model to the inherent dynamics of time series data-a timely reminder that in the realm of LTSF, bigger is not invariably better.

Time Series Time Series Forecasting

LLMLight: Large Language Models as Traffic Signal Control Agents

1 code implementation26 Dec 2023 Siqi Lai, Zhao Xu, Weijia Zhang, Hao liu, Hui Xiong

Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion.

Decision Making Management +1

Large Language Models are Not Stable Recommender Systems

no code implementations25 Dec 2023 TianHui Ma, Yuan Cheng, HengShu Zhu, Hui Xiong

With the significant successes of large language models (LLMs) in many natural language processing tasks, there is growing interest among researchers in exploring LLMs for novel recommender systems.

Recommendation Systems

Survey on Trustworthy Graph Neural Networks: From A Causal Perspective

1 code implementation19 Dec 2023 Wenzhao Jiang, Hao liu, Hui Xiong

Moreover, we introduce a taxonomy of Causality-Inspired GNNs (CIGNNs) based on the type of causal learning capability they are equipped with, i. e., causal reasoning and causal representation learning.

Graph Mining Representation Learning

A Survey of Text Watermarking in the Era of Large Language Models

no code implementations13 Dec 2023 Aiwei Liu, Leyi Pan, Yijian Lu, Jingjing Li, Xuming Hu, Xi Zhang, Lijie Wen, Irwin King, Hui Xiong, Philip S. Yu

Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically.

Dialogue Generation

GeoDeformer: Geometric Deformable Transformer for Action Recognition

no code implementations29 Nov 2023 Jinhui Ye, Jiaming Zhou, Hui Xiong, Junwei Liang

Specifically, at the core of GeoDeformer is the Geometric Deformation Predictor, a module designed to identify and quantify potential spatial and temporal geometric deformations within the given video.

Action Recognition

The Impact of Generative Artificial Intelligence

no code implementations13 Nov 2023 Kaichen Zhang, Ohchan Kwon, Hui Xiong

The rise of generative artificial intelligence (AI) has sparked concerns about its potential influence on unemployment and market depression.

Causal Inference

DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops

no code implementations10 Nov 2023 Hangtong Xu, Yuanbo Xu, Yongjian Yang, Fuzhen Zhuang, Hui Xiong

We demonstrate theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms.

Recommendation Systems

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

no code implementations23 Oct 2023 Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, Hao Wang

To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game.

Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment

1 code implementation21 Oct 2023 Chuang Zhao, Hongke Zhao, HengShu Zhu, Zhenya Huang, Nan Feng, Enhong Chen, Hui Xiong

One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.

Contrastive Learning Learning Theory +1

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

2 code implementations16 Oct 2023 Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, XiaoLi Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong

In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.

Time Series Time Series Analysis

Machine Learning for Urban Air Quality Analytics: A Survey

no code implementations14 Oct 2023 Jindong Han, Weijia Zhang, Hao liu, Hui Xiong

In this article, we present a comprehensive survey of ML-based air quality analytics, following a roadmap spanning from data acquisition to pre-processing, and encompassing various analytical tasks such as pollution pattern mining, air quality inference, and forecasting.

Air Quality Inference

Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution

no code implementations27 Sep 2023 Ying Sun, HengShu Zhu, Hui Xiong

Self-interpreting neural networks have garnered significant interest in research.

Semi-supervised Domain Adaptation in Graph Transfer Learning

no code implementations19 Sep 2023 Ziyue Qiao, Xiao Luo, Meng Xiao, Hao Dong, Yuanchun Zhou, Hui Xiong

To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding, thus the node classifier trained on labeled source nodes can be transferred to the target nodes.

Semi-supervised Domain Adaptation Transfer Learning +1

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network

no code implementations31 Aug 2023 Weijia Zhang, Le Zhang, Jindong Han, Hao liu, Jingbo Zhou, Yu Mei, Hui Xiong

Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system.

Time Series Time Series Forecasting

DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model

1 code implementation18 Aug 2023 Siyang Li, Hui Xiong, Yize Chen

Recent proliferation of electric vehicle (EV) charging events has brought prominent stress over power grid operation.

Denoising Management +1

Digital twin brain: a bridge between biological intelligence and artificial intelligence

no code implementations3 Aug 2023 Hui Xiong, Congying Chu, Lingzhong Fan, Ming Song, JiaQi Zhang, Yawei Ma, Ruonan Zheng, Junyang Zhang, Zhengyi Yang, Tianzi Jiang

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems.

Generative Job Recommendations with Large Language Model

no code implementations5 Jul 2023 Zhi Zheng, Zhaopeng Qiu, Xiao Hu, Likang Wu, HengShu Zhu, Hui Xiong

The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.

Collaborative Filtering Language Modelling +3

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

no code implementations3 Jul 2023 Chuan Qin, Le Zhang, Rui Zha, Dazhong Shen, Qi Zhang, Ying Sun, Chen Zhu, HengShu Zhu, Hui Xiong

To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management.

Decision Making Management

Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction

no code implementations30 Jun 2023 Qizhi Wan, Changxuan Wan, Keli Xiao, Hui Xiong, Dexi Liu, Xiping Liu

This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module.

Document-level Event Extraction Event Extraction +2

Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

1 code implementation29 Jun 2023 Meng Xiao, Dongjie Wang, Min Wu, Kunpeng Liu, Hui Xiong, Yuanchun Zhou, Yanjie Fu

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features.

Feature Engineering Q-Learning

Spatial Heterophily Aware Graph Neural Networks

1 code implementation21 Jun 2023 Congxi Xiao, Jingbo Zhou, Jizhou Huang, Tong Xu, Hui Xiong

However, urban graphs usually can be observed to possess a unique spatial heterophily property; that is, the dissimilarity of neighbors at different spatial distances can exhibit great diversity.

HomoGCL: Rethinking Homophily in Graph Contrastive Learning

1 code implementation16 Jun 2023 Wen-Zhi Li, Chang-Dong Wang, Hui Xiong, Jian-Huang Lai

Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting" learning scheme.

Contrastive Learning Self-Supervised Learning

GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification

1 code implementation16 Jun 2023 Wen-Zhi Li, Chang-Dong Wang, Hui Xiong, Jian-Huang Lai

Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios.

Blocking Classification +1

Multi-Temporal Relationship Inference in Urban Areas

1 code implementation15 Jun 2023 Shuangli Li, Jingbo Zhou, Ji Liu, Tong Xu, Enhong Chen, Hui Xiong

Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL).

Graph Learning Representation Learning +1

A Survey on Large Language Models for Recommendation

1 code implementation31 May 2023 Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, HengShu Zhu, Qi Liu, Hui Xiong, Enhong Chen

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).

Recommendation Systems Self-Supervised Learning

Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedback

no code implementations25 May 2023 Yiqi Lin, Hao Wu, Ruichen Wang, Haonan Lu, Xiaodong Lin, Hui Xiong, Lin Wang

Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space.

TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation

1 code implementation24 May 2023 Yiheng Jiang, Yuanbo Xu, Yongjian Yang, Funing Yang, Pengyang Wang, Hui Xiong

In this paper, we present a MLP-like architecture for sequential recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token communications.

Sequential Recommendation

Preference or Intent? Double Disentangled Collaborative Filtering

no code implementations18 May 2023 Chao Wang, HengShu Zhu, Dazhong Shen, Wei Wu, Hui Xiong

In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences.

Collaborative Filtering Disentanglement +1

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

1 code implementation18 May 2023 Chenguang Du, Kaichun Yao, HengShu Zhu, Deqing Wang, Fuzhen Zhuang, Hui Xiong

However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing.

Information Retrieval Representation Learning +1

A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

no code implementations4 Feb 2023 Kun Yi, Qi Zhang, Longbing Cao, Shoujin Wang, Guodong Long, Liang Hu, Hui He, Zhendong Niu, Wei Fan, Hui Xiong

Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.

Time Series Time Series Analysis

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach

1 code implementation5 Jan 2023 Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Jingbo Zhou, Dejing Dou, Hui Xiong

Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.

Descriptive Language Modelling +1

Pontryagin Optimal Control via Neural Networks

1 code implementation30 Dec 2022 Chengyang Gu, Hui Xiong, Yize Chen

Solving real-world optimal control problems are challenging tasks, as the complex, high-dimensional system dynamics are usually unrevealed to the decision maker.

Model-based Reinforcement Learning Reinforcement Learning (RL)

A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection

no code implementations26 Nov 2022 Congxi Xiao, Jingbo Zhou, Jizhou Huang, HengShu Zhu, Tong Xu, Dejing Dou, Hui Xiong

The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions.

Specificity

MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning

2 code implementations16 Sep 2022 Jiangmeng Li, Wenwen Qiang, Yanan Zhang, Wenyi Mo, Changwen Zheng, Bing Su, Hui Xiong

As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample.

Contrastive Learning Meta-Learning +1

Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification

no code implementations16 Sep 2022 Meng Xiao, Ziyue Qiao, Yanjie Fu, Hao Dong, Yi Du, Pengyang Wang, Hui Xiong, Yuanchun Zhou

Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals.

Classification

Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

2 code implementations16 Sep 2022 Jiangmeng Li, Wenwen Qiang, Changwen Zheng, Bing Su, Farid Razzak, Ji-Rong Wen, Hui Xiong

To this end, we propose a methodology, specifically consistency and complementarity network (CoCoNet), which avails of strict global inter-view consistency and local cross-view complementarity preserving regularization to comprehensively learn representations from multiple views.

Representation Learning Self-Supervised Learning

Self-Optimizing Feature Transformation

no code implementations16 Sep 2022 Meng Xiao, Dongjie Wang, Min Wu, Kunpeng Liu, Hui Xiong, Yuanchun Zhou, Yanjie Fu

Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features.

Feature Engineering Outlier Detection

CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences

1 code implementation ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2022 Shengming Zhang, Yanchi Liu, Xuchao Zhang, Wei Cheng, Haifeng Chen, Hui Xiong

It is critical and important to detect anomalies in event sequences, which becomes widely available in many application domains. In-deed, various efforts have been made to capture abnormal patterns from event sequences through sequential pattern analysis or event representation learning. However, existing approaches usually ignore the semantic information of event content. To this end, in this paper, we propose a self-attentive encoder-decoder transformer framework, Content-Aware Transformer(CAT), for anomaly detection in event sequences. In CAT, the encoder learns preamble event sequence representations with content awareness, and the decoder embeds sequences under detection into a latent space, where anomalies are distinguishable. Specifically, the event content is first fed to a content-awareness layer, generating representations of each event. The encoder accepts preamble event representation sequence, generating feature maps. In the decoder, an additional token is added at the beginning of the sequence under detection, denoting the sequence status. A one-class objective together with sequence reconstruction loss is collectively applied to train our framework under the label efficiency scheme. Furthermore, CAT is optimized under a scalable and efficient setting. Finally, extensive experiments on three real-world datasets demonstrate the superiority of CAT.

Anomaly Detection

Customized Conversational Recommender Systems

no code implementations30 Jun 2022 Shuokai Li, Yongchun Zhu, Ruobing Xie, Zhenwei Tang, Zhao Zhang, Fuzhen Zhuang, Qing He, Hui Xiong

In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.

Meta-Learning Recommendation Systems

Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction

1 code implementation30 Jun 2022 Liangzhe Han, Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, Hui Xiong

Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society.

Graph Representation Learning

Interventional Contrastive Learning with Meta Semantic Regularizer

no code implementations29 Jun 2022 Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Bing Su, Hui Xiong

Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner.

Contrastive Learning Representation Learning +1

Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting

1 code implementation28 Jun 2022 Junchen Ye, Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, Hui Xiong

To equip the graph neural network with a flexible and practical graph structure, in this paper, we investigate how to model the evolutionary and multi-scale interactions of time series.

Multivariate Time Series Forecasting Self-Learning +1

Priors in Deep Image Restoration and Enhancement: A Survey

1 code implementation4 Jun 2022 Yunfan Lu, Yiqi Lin, Hao Wu, Yunhao Luo, Xu Zheng, Hui Xiong, Lin Wang

Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation.

Image Restoration

Detect Professional Malicious User with Metric Learning in Recommender Systems

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

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

Metric Learning Outlier Detection +1

Reinforced Imitative Graph Learning for Mobile User Profiling

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

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

Graph Learning

MetAug: Contrastive Learning via Meta Feature Augmentation

2 code implementations10 Mar 2022 Jiangmeng Li, Wenwen Qiang, Changwen Zheng, Bing Su, Hui Xiong

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

Contrastive Learning Informativeness +1

Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation

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

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

Unsupervised Domain Adaptation

Hyperbolic Graph Neural Networks: A Review of Methods and Applications

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

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

Anatomy Graph Learning

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

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

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

reinforcement-learning Reinforcement Learning (RL)

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

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

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

Learning to Walk with Dual Agents for Knowledge Graph Reasoning

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

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

reinforcement-learning Reinforcement Learning (RL)

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

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

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

counterfactual Data Augmentation

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

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

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

Decision Making

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

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

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

Variational Inference

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

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

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

Entity Alignment

Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness

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

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

Variational Inference

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

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

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

Image Captioning Informativeness +2

Domain-Invariant Representation Learning with Global and Local Consistency

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

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

Representation Learning Unsupervised Domain Adaptation

Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design

no code implementations INFORMS Journal 2021 Junming Liu, Weiwei Chen, Jingyuan Yang, Hui Xiong, Can Chen

Summary of Contribution: We propose an iterative prediction-and-optimization algorithm for multilevel distribution network design for e-logistics and evaluate its operational value for online retailers.

GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

1 code implementation24 Sep 2021 Shuangli Li, Jingbo Zhou, Tong Xu, Dejing Dou, Hui Xiong

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

Contrastive Learning Data Augmentation +4

Adversarial Neural Trip Recommendation

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

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

Recommendation Systems

Information Theory-Guided Heuristic Progressive Multi-View Coding

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

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

Contrastive Learning MULTI-VIEW LEARNING +1

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

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

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

Drug Discovery Graph Attention +1

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

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

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

Decision Making Graph Representation Learning +1

Deep Subdomain Adaptation Network for Image Classification

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

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

Classification Domain Adaptation +4

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

1 code implementation14 Jun 2021 Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu

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

Graph Anomaly Detection

Heterogeneous Graph Representation Learning with Relation Awareness

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

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

Graph Learning Graph Representation Learning +4

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

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

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

Multi-agent Reinforcement Learning reinforcement-learning +1

Out-of-Town Recommendation with Travel Intention Modeling

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

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

point of interests

CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation

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

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

Decision Making Q-Learning +1

Spatial Object Recommendation with Hints: When Spatial Granularity Matters

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

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

Attribute Multi-Task Learning +2

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

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

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

Graph Learning Multi-Task Learning

Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning

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

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

Graph Learning Node Property Prediction +1

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

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

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

Drug Discovery Graph Attention +2

Coupled Layer-wise Graph Convolution for Transportation Demand Prediction

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

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

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

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

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

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

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

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

Feature Importance feature selection +2

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

no code implementations16 Sep 2020 Denghui Zhang, Junming Liu, HengShu Zhu, Yanchi Liu, Lichen Wang, Pengyang Wang, Hui Xiong

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

Benchmarking Link Prediction +2

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

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

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

Aspect Extraction Denoising +4

Learning Adaptive Embedding Considering Incremental Class

1 code implementation31 Aug 2020 Yang Yang, Zhen-Qiang Sun, HengShu Zhu, Yanjie Fu, Hui Xiong, Jian Yang

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

Class Incremental Learning Clustering +1

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

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

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

General Classification Knowledge Distillation +2

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

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

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

Predicting Temporal Sets with Deep Neural Networks

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

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

Time Series Analysis

Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems

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

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

Clustering Management

A Survey on Knowledge Graph-Based Recommender Systems

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

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

Explainable Recommendation Recommendation Systems

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

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

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

Collaborative Ranking Recommendation Systems

Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling

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

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

Image Retrieval Management +3

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

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

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

Clustering

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

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

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

BIG-bench Machine Learning Ensemble Learning

A Comprehensive Survey on Transfer Learning

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

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

Transfer Learning

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

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

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

Management Multi-agent Reinforcement Learning +1

EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction

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

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

Knowledge Tracing

Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation

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

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

Transfer Learning

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

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

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

Adversarial Attack

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

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

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

FineFool: Fine Object Contour Attack via Attention

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

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

Adversarial Attack Object

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

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

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

Data Visualization Representation Learning

Risk-Averse Classification

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

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

Binary Classification Classification +1

Recruitment Market Trend Analysis with Sequential Latent Variable Models

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

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

REMIX: Automated Exploration for Interactive Outlier Detection

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

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

Meta-Learning Outlier Detection

Dynamic Word Embeddings for Evolving Semantic Discovery

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

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

Representation Learning Word Embeddings

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

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

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

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

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

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

Information Retrieval Metric Learning +1

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