Search Results for author: Ling Chen

Found 85 papers, 48 papers with code

Motion Avatar: Generate Human and Animal Avatars with Arbitrary Motion

1 code implementation18 May 2024 Zeyu Zhang, Yiran Wang, Biao Wu, Shuo Chen, Zhiyuan Zhang, Shiya Huang, Wenbo Zhang, Meng Fang, Ling Chen, Yang Zhao

Firstly, we proposed a novel agent-based approach named Motion Avatar, which allows for the automatic generation of high-quality customizable human and animal avatars with motions through text queries.

Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks

no code implementations17 May 2024 Rongrong Ma, Guansong Pang, Ling Chen

In this work we propose a novel approach, namely collective structure knowledge-augmented graph neural network (CoS-GNN), in which a new message passing method is introduced to allow GNNs to harness a diverse set of node- and graph-level structure features, together with original node features/attributes, in augmented graphs.

Anomaly Detection Data Augmentation +3

Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks

no code implementations8 May 2024 Rongrong Ma, Guansong Pang, Ling Chen

Extensive experiments on 16 imbalanced graph datasets show that MOSGNN i) significantly outperforms five state-of-the-art models, and ii) offers a generic framework, in which different advanced imbalanced learning loss functions can be easily plugged in and obtain significantly improved classification performance.

Graph Classification Graph Learning +1

Interest Clock: Time Perception in Real-Time Streaming Recommendation System

no code implementations30 Apr 2024 Yongchun Zhu, Jingwu Chen, Ling Chen, Yitan Li, Feng Zhang, Zuotao Liu

Time modeling aims to enable recommendation systems to perceive time changes to capture users' dynamic preferences over time, which is an important and challenging problem in recommendation systems.

Recommendation Systems

MMAC-Copilot: Multi-modal Agent Collaboration Operating System Copilot

no code implementations28 Apr 2024 Zirui Song, Yaohang Li, Meng Fang, Zhenhao Chen, Zecheng Shi, Yuan Huang, Ling Chen

Autonomous virtual agents are often limited by their singular mode of interaction with real-world environments, restricting their versatility.


ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction

1 code implementation22 Apr 2024 Yi Rong, Yingchi Mao, Yinqiu Liu, Ling Chen, Xiaoming He, Dusit Niyato

However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i. e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush.

Graph Generation

Graph Continual Learning with Debiased Lossless Memory Replay

no code implementations17 Apr 2024 Chaoxi Niu, Guansong Pang, Ling Chen

Graph continual learning (GCL) tackles this problem by continually adapting GNNs to the expanded graph of the current task while maintaining the performance over the graph of previous tasks.

Continual Learning Incremental Learning

DSGNN: A Dual-View Supergrid-Aware Graph Neural Network for Regional Air Quality Estimation

no code implementations2 Apr 2024 Xin Zhang, Ling Chen, Xing Tang, Hongyu Shi

To this end, we propose a Dual-view Supergrid-aware Graph Neural Network (DSGNN) for regional air quality estimation, which can model the spatial dependencies of distant grid regions from dual views (i. e., satellite-derived aerosol optical depth (AOD) and meteorology).

D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting

1 code implementation26 Mar 2024 Xiaobing Yuan, Ling Chen

A decomposition-reconstruction-decomposition (D-R-D) module is proposed to progressively extract the information of frequencies mixed in the components, corresponding to the "deep" aspect.

Time Series Time Series Forecasting

Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data

no code implementations2 Mar 2024 Jun-En Ding, Phan Nguyen Minh Thao, Wen-Chih Peng, Jian-Zhe Wang, Chun-Cheng Chug, Min-Chen Hsieh, Yun-Chien Tseng, Ling Chen, Dongsheng Luo, Chi-Te Wang, Pei-fu Chen, Feng Liu, Fang-Ming Hung

In our experiments, we observe that clinicalBERT and PubMed-BERT, when combined with attention fusion, can achieve an accuracy of 73% in multiclass chronic diseases and diabetes prediction.

Diabetes Prediction

RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering

1 code implementation26 Feb 2024 Zihan Zhang, Meng Fang, Ling Chen

Based on our findings, we propose Time-Aware Adaptive Retrieval (TA-ARE), a simple yet effective method that helps LLMs assess the necessity of retrieval without calibration or additional training.

Open-Domain Question Answering Retrieval

SA-MDKIF: A Scalable and Adaptable Medical Domain Knowledge Injection Framework for Large Language Models

no code implementations1 Feb 2024 Tianhan Xu, Zhe Hu, Ling Chen, Bin Li

In the next stage, we train the skill router using task-specific downstream data and use this router to integrate the acquired skills with LLMs during inference.

Continuously Evolving Graph Neural Controlled Differential Equations for Traffic Forecasting

no code implementations26 Jan 2024 Jiajia Wu, Ling Chen

In this paper, we propose Continuously Evolving Graph Neural Controlled Differential Equations (CEGNCDE) to capture continuous temporal dependencies and spatial dependencies over time simultaneously.

Large Language Models Are Neurosymbolic Reasoners

1 code implementation17 Jan 2024 Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang

A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning.

Common Sense Reasoning Math +2

MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting

no code implementations17 Jan 2024 Zongjiang Shang, Ling Chen

In addition, a tri-stage message passing mechanism is introduced to aggregate pattern information and learn the interaction strength between temporal patterns of different scales.

Time Series Time Series Forecasting

Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Game

no code implementations29 Dec 2023 Zijing Shi, Meng Fang, Shunfeng Zheng, Shilong Deng, Ling Chen, Yali Du

This problem motivates the area of ad hoc teamwork, in which an agent may potentially cooperate with a variety of teammates to achieve a shared goal.

Brain Diffuser with Hierarchical Transformer for MCI Causality Analysis

no code implementations14 Dec 2023 Qiankun Zuo, Ling Chen, Shuqiang Wang

It can captures both unidirectal and bidirectional interactions between brain regions, providing a comprehensive understanding of the brain's information processing mechanisms.

Connectivity Estimation Denoising +1

TPRNN: A Top-Down Pyramidal Recurrent Neural Network for Time Series Forecasting

no code implementations11 Dec 2023 Ling Chen, Jiahua Cui

Then by executing a multi-scale information interaction module from top to bottom, we model both the temporal dependencies of each scale and the influences of subsequences of different scales, resulting in a complete modeling of multi-scale temporal patterns in time series.

Decision Making Time Series +1

Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning

no code implementations19 Nov 2023 Ling Chen, Zhishen Huang, Yong Long, Saiprasad Ravishankar

In our experiments, we study combinations of supervised deep network reconstructors and MBIR solver with learned sparse representation-based priors or analytical priors.

Computed Tomography (CT) Image Reconstruction

CITB: A Benchmark for Continual Instruction Tuning

1 code implementation23 Oct 2023 Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad

In this work, we establish a CIT benchmark consisting of learning and evaluation protocols.

Continual Learning

How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

1 code implementation11 Oct 2023 Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad, Jun Wang

Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.

World Knowledge

WeatherGNN: Exploiting Complicated Relationships in Numerical Weather Prediction Bias Correction

no code implementations9 Oct 2023 Binqing Wu, Weiqi Chen, Wengwei Wang, Bingqing Peng, Liang Sun, Ling Chen

In addition, the interactions between weather factors are further complicated by the spatial dependencies between regions, which are influenced by varied terrain and atmospheric motions.

Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction

1 code implementation NeurIPS 2023 Zechuan Zhang, Li Sun, Zongxin Yang, Ling Chen, Yi Yang

Reconstructing 3D clothed human avatars from single images is a challenging task, especially when encountering complex poses and loose clothing.


ABS-SGD: A Delayed Synchronous Stochastic Gradient Descent Algorithm with Adaptive Batch Size for Heterogeneous GPU Clusters

no code implementations29 Aug 2023 Xin Zhou, Ling Chen, Houming Wu

In this paper, we propose a delayed synchronous SGD algorithm with adaptive batch size (ABS-SGD) for heterogeneous GPU clusters.

HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks

1 code implementation28 Aug 2023 Jiaxi Li, Guansong Pang, Ling Chen, Mohammad-Reza Namazi-Rad

To address the problem, we propose HRGCN, an unsupervised deep heterogeneous graph neural network, to model complex heterogeneous relations between different entities in the system for effectively identifying these anomalous behaviour graphs.

Anomaly Detection Relation

CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection Method

1 code implementation7 Jul 2023 Ling Chen, Chaodu Song, Xu Wang, Dachao Fu, Feifei Li

To this end, we propose CSCLog, a Component Subsequence Correlation-Aware Log anomaly detection method, which not only captures the sequential dependencies in subsequences, but also models the implicit correlations of subsequences.

Anomaly Detection

Graph-level Anomaly Detection via Hierarchical Memory Networks

1 code implementation3 Jul 2023 Chaoxi Niu, Guansong Pang, Ling Chen

One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole.

Anomaly Detection

DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting

no code implementations2 Jul 2023 Binqing Wu, Ling Chen

Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network.

graph construction

CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models

1 code implementation18 May 2023 Jiaxu Zhao, Meng Fang, Zijing Shi, Yitong Li, Ling Chen, Mykola Pechenizkiy

We evaluate two popular pretrained Chinese conversational models, CDial-GPT and EVA2. 0, using CHBias.

Response Generation

Disentangled Pre-training for Image Matting

1 code implementation3 Apr 2023 Yanda Li, Zilong Huang, Gang Yu, Ling Chen, Yunchao Wei, Jianbo Jiao

The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective.

Disentanglement Image Matting

GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method

1 code implementation22 Feb 2023 Xing Tang, Ling Chen

GTRL is the first work that incorporates the entity group modeling to capture the correlation between entities by stacking only a finite number of layers.

Link Prediction

Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning

1 code implementation31 Jan 2023 Chaoxi Niu, Guansong Pang, Ling Chen

To tackle this problem, this article proposes a novel approach that builds a discriminative model on collective affinity information (i. e., two sets of pairwise affinities between the negative instances and the anchor instance) to mine hard negatives in GCL.

Contrastive Learning Node Classification

Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE

no code implementations31 Dec 2022 Jun-En Ding, Chih-Ho Hsu, Kuan-Chia Ling, Ling Chen, Fang-Ming Hung

The proposed models achieved AUC scores above 0. 83 for hospital transfer risk prediction based on the measurements of past 1, 2, and 3 days, outperforming baseline machine learning methods.

Survival Analysis

Mask Matching Transformer for Few-Shot Segmentation

1 code implementation5 Dec 2022 Siyu Jiao, Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Humphrey Shi

Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results.

Few-Shot Semantic Segmentation Segmentation

SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition

1 code implementation25 Nov 2022 Rong Hu, Ling Chen, Shenghuan Miao, Xing Tang

SWL-Adapt calculates sample weights according to the classification loss and domain discrimination loss of each sample with a parameterized network.

Human Activity Recognition Unsupervised Domain Adaptation

Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting

2 code implementations5 Aug 2022 Juyong Jiang, Binqing Wu, Ling Chen, Kai Zhang, Sunghun Kim

On the one hand, our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions; on the other hand, it uses GAN structure to systematically evaluate statistical consistencies between the real and the predicted time series in terms of both the temporal trending and the complex spatial-temporal dependencies.

Time Series Time Series Analysis

Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection

no code implementations29 May 2022 Shaoshen Wang, Yanbin Liu, Ling Chen, Chengqi Zhang

Empirically, DERM outperformed the state-of-the-art on the unsupervised AD benchmark consisting of 18 datasets.

Unsupervised Anomaly Detection

Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction

no code implementations19 May 2022 Ling Chen, Zhishen Huang, Yong Long, Saiprasad Ravishankar

Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise.

Computed Tomography (CT) Image Reconstruction

Fine-Grained Population Mobility Data-Based Community-Level COVID-19 Prediction Model

1 code implementation13 Feb 2022 Pengyue Jia, Ling Chen, Dandan Lyu

Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geographic units.

Informative Pseudo-Labeling for Graph Neural Networks with Few Labels

no code implementations20 Jan 2022 Yayong Li, Jie Yin, Ling Chen

It aims to augment the training set with pseudo-labeled unlabeled nodes with high confidence so as to re-train a supervised model in a self-training cycle.

Informativeness Node Classification +1

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

2 code implementations13 Jan 2022 Ling Chen, Donghui Chen, Zongjiang Shang, Binqing Wu, Cen Zheng, Bo Wen, Wei zhang

Given the multi-scale feature representations and scale-specific inter-variable dependencies, a multi-scale temporal graph neural network is introduced to jointly model intra-variable dependencies and inter-variable dependencies.

Graph Learning Multivariate Time Series Forecasting +1

Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

1 code implementation19 Dec 2021 Rongrong Ma, Guansong Pang, Ling Chen, Anton Van Den Hengel

Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs.

Anomaly Detection Knowledge Distillation

Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting

no code implementations9 Nov 2021 Ling Chen, Weiqi Chen, Binqing Wu, Youdong Zhang, Bo Wen, Chenghu Yang

Time series forecasting is a significant problem in many applications, e. g., financial predictions and business optimization.

Decoder Quantization +2

RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining

1 code implementation1 Nov 2021 Ling Chen, Jun Cui, Xing Tang, Chaodu Song, Yuntao Qian, Yansheng Li, Yongjun Zhang

Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings.

Graph Representation Learning Knowledge Graph Completion

TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation

1 code implementation26 Oct 2021 Ling Chen, Da Wang, Dandan Lyu, Xing Tang, Hongyu Shi

Evolving temporal networks serve as the abstractions of many real-life dynamic systems, e. g., social network and e-commerce.

Link Prediction Network Embedding +1

Multi-Relation Aware Temporal Interaction Network Embedding

1 code implementation9 Oct 2021 Ling Chen, Shanshan Yu, Dandan Lyu, Da Wang

However, existing temporal interaction network embedding methods only use historical interaction relations to mine neighbor nodes, ignoring other relation types.

Graph Attention Network Embedding +1

Goal Randomization for Playing Text-based Games without a Reward Function

no code implementations29 Sep 2021 Meng Fang, Yunqiu Xu, Yali Du, Ling Chen, Chengqi Zhang

In a variety of text-based games, we show that this simple method results in competitive performance for agents.

Decision Making text-based games

Generalization in Text-based Games via Hierarchical Reinforcement Learning

1 code implementation Findings (EMNLP) 2021 Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Chengqi Zhang

Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents.

Hierarchical Reinforcement Learning reinforcement-learning +2

Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged Photos

1 code implementation17 Sep 2021 Ling Chen, Dandan Lyu, Shanshan Yu, Gencai Chen

In addition, to capture the significances of different photos, we exploit the self-attention mechanism to obtain the visual representations of users and tourist attractions.

Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting

1 code implementation27 Aug 2021 Ling Chen, Jiahui Xu, Binqing Wu, Yuntao Qian, Zhenhong Du, Yansheng Li, Yongjun Zhang

The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively.

graph construction

SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity Recognition

1 code implementation17 Aug 2021 Ling Chen, Yi Zhang, Shenghuan Miao, Sirou Zhu, Rong Hu, Liangying Peng, Mingqi Lv

In order to address the challenge that the transferabilities of different sensors are different, we propose SALIENCE (unsupervised user adaptation model for multiple wearable sensors based human activity recognition) model.

Human Activity Recognition

DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine Model

1 code implementation5 Apr 2021 Ling Chen, Hongyu Shi

In this paper, an ensemble diversity enhanced extreme deep factorization machine model (DexDeepFM) is proposed, which designs the ensemble diversity measure in each hidden layer and considers both ensemble diversity and prediction accuracy in the objective function.

Homophily Outlier Detection in Non-IID Categorical Data

no code implementations21 Mar 2021 Guansong Pang, Longbing Cao, Ling Chen

Most of existing outlier detection methods assume that the outlier factors (i. e., outlierness scoring measures) of data entities (e. g., feature values and data objects) are Independent and Identically Distributed (IID).

feature selection Outlier Detection

Unified Robust Training for Graph NeuralNetworks against Label Noise

no code implementations5 Mar 2021 Yayong Li, Jie Yin, Ling Chen

Learning with label noise has been primarily studied in the context of image classification, but these techniques cannot be directly applied to graph-structured data, due to two major challenges -- label sparsity and label dependency -- faced by learning on graphs.

Learning with noisy labels Node Classification

Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective

no code implementations ICLR 2022 Wei Huang, Yayong Li, Weitao Du, Jie Yin, Richard Yi Da Xu, Ling Chen, Miao Zhang

Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally.

HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method

1 code implementation12 Jan 2021 Jiahui Xu, Ling Chen, Mingqi Lv, Chaoqun Zhan, Sanjian Chen, Jian Chang

Existing air quality forecasting methods cannot effectively model the diffusion processes of air pollutants between cities and monitoring stations, which may suddenly deteriorate the air quality of a region.


A Multi-Mode Modulator for Multi-Domain Few-Shot Classification

1 code implementation ICCV 2021 Yanbin Liu, Juho Lee, Linchao Zhu, Ling Chen, Humphrey Shi, Yi Yang

Most existing few-shot classification methods only consider generalization on one dataset (i. e., single-domain), failing to transfer across various seen and unseen domains.

Classification Domain Generalization

Histopathology of Third Trimester Placenta from SARS-CoV-2-Positive Women

no code implementations5 Aug 2020 Mai He, Priya Skaria, Kasey Kreutz, Ling Chen, Ian Hagemann, Ebony B. Carter, Indira U. Mysorekar, D Michael Nelson, John Pfeifer, Louis P. Dehner

Results: Twenty-one 3rd-trimester, placentas from SARS-CoV-2-positive women were identified and compared to 20 placentas from SARS-CoV-2-negative women.

Smoothing Graphons for Modelling Exchangeable Relational Data

no code implementations25 Feb 2020 Xuhui Fan, Yaqiong Li, Ling Chen, Bin Li, Scott A. Sisson

We initially propose the Integrated Smoothing Graphon (ISG) which introduces one smoothing parameter to the SBM graphon to generate continuous relational intensity values.

Link Prediction Stochastic Block Model

Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling

no code implementations24 Feb 2020 Yaqiong Li, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, Scott A. Sisson

In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data.

Link Prediction

Relational State-Space Model for Stochastic Multi-Object Systems

no code implementations ICLR 2020 Fan Yang, Ling Chen, Fan Zhou, Yusong Gao, Wei Cao

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.

Object Time Series +1

Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

1 code implementation27 Nov 2019 Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions.


SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs

no code implementations22 Aug 2019 Yayong Li, Jie Yin, Ling Chen

In this paper, we propose a SEmi-supervised Adversarial active Learning (SEAL) framework on attributed graphs, which fully leverages the representation power of deep neural networks and devises a novel AL query strategy in an adversarial way.

Active Learning Graph Embedding +1

A Review for Weighted MinHash Algorithms

1 code implementation12 Nov 2018 Wei Wu, Bin Li, Ling Chen, Junbin Gao, Chengqi Zhang

In this review, we mainly categorize the Weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash algorithms to real-valued weighted MinHash ones (particularly the Consistent Weighted Sampling scheme).

Data Structures and Algorithms

Binarized Attributed Network Embedding

2 code implementations ICDM 2018 Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang

To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.

Graph Embedding Link Prediction +2

Privacy-preserving Stochastic Gradual Learning

no code implementations30 Sep 2018 Bo Han, Ivor W. Tsang, Xiaokui Xiao, Ling Chen, Sai-fu Fung, Celina P. Yu

PRESTIGE bridges private updates of the primal variable (by private sampling) with the gradual curriculum learning (CL).

Privacy Preserving Stochastic Optimization

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

3 code implementations13 Jun 2018 Guansong Pang, Longbing Cao, Ling Chen, Huan Liu

However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).

Anomaly Detection Disease Prediction +3

Improved Consistent Weighted Sampling Revisited

1 code implementation5 Jun 2017 Wei Wu, Bin Li, Ling Chen, Chengqi Zhang, Philip S. Yu

Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets.

Data Structures and Algorithms

Personalized Video Recommendation Using Rich Contents from Videos

1 code implementation21 Dec 2016 Xingzhong Du, Hongzhi Yin, Ling Chen, Yang Wang, Yi Yang, Xiaofang Zhou

In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features.

Recommendation Systems

On the Convergence of A Family of Robust Losses for Stochastic Gradient Descent

no code implementations5 May 2016 Bo Han, Ivor W. Tsang, Ling Chen

The convergence of Stochastic Gradient Descent (SGD) using convex loss functions has been widely studied.

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