67 citations · 243 across the 18 of their papers we have counts for
18 papers
Unsupervised feature selection via self-paced learning and low-redundant regularization
Weiyi Li, Hongmei Chen, Tianrui Li +2
Much more attention has been paid to unsupervised feature selection nowadays due to the emergence of massive unlabeled data. The distribution of samples and the latent effect of tr…
Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection
Yanyong Huang, Zongxin Shen, Fuxu Cai +2
Unsupervised feature selection is an important method to reduce dimensions of high dimensional data without labels, which is benefit to avoid ``curse of dimensionality'' and improv…
Fairness and Accuracy in Federated Learning
Wei Huang, Tianrui Li, Dexian Wang +2
In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure priv…
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis
Huaishao Luo, Lei Ji, Tianrui Li +2
In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks…
ReAD: A Regional Anomaly Detection Framework Based on Dynamic Partition
Huaishao Luo, Chuishi Meng, Bowen Wu +3
The detection of the abnormal area from urban data is a significant research problem. However, to the best of our knowledge, previous methods designed on spatio-temporal anomalies…
Distributional Discrepancy: A Metric for Unconditional Text Generation
Ping Cai, Xingyuan Chen, Peng Jin +2
The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However,…
Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories
Huajun He, Ruiyuan Li, Rubin Wang +3
Virus transmission from person to person is an emergency event facing the global public. Early detection and isolation of potentially susceptible crowds can effectively control the…
Learning with Noisy Labels for Sentence-level Sentiment Classification
Hao Wang, Bing Liu, Chaozhuo Li +2
Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the…
Urban flows prediction from spatial-temporal data using machine learning: A survey
Peng Xie, Tianrui Li, Jia Liu +3
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors,…
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
Huaishao Luo, Tianrui Li, Bing Liu +1
This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity…
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework
Shengdong Du, Tianrui Li, Yan Yang +1
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air qu…
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
Jielei Chu, Hongjun Wang, Jing Liu +2
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering i…
Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation
Huaishao Luo, Tianrui Li, Bing Liu +2
Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that using dependency tree structure representation is promi…
A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
Shengdong Du, Tianrui Li, Xun Gong +1
Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traff…
Three-Stream Convolutional Networks for Video-based Person Re-Identification
Zeng Yu, Tianrui Li, Ning Yu +3
This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks. To this end, we study a number of methods for video-based person…
Reconstruction of Hidden Representation for Robust Feature Extraction
Zeng Yu, Tianrui Li, Ning Yu +3
This paper aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretical summarize the general properties of…
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks
Junbo Zhang, Yu Zheng, Dekang Qi +3
Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dep…
Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints
Jielei Chu, Hongjun Wang, Hua Meng +2
Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach…